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Jiawei Han
 
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Views: 1626 SonicNU
Lecture - 34 Data Mining and Knowledge Discovery
 
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Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 133239 nptelhrd
¿Qué es la minería de datos?
 
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En esta época mucha de la información que tenemos es digital, todos eso datos nos cuentan historias y nos dan a entender mejor al mundo ya que ocultan un patrón que nos puede revelar algo que no sabíamos... pero como revelamos ese patron? Ayúdame en Patreon: https://goo.gl/GYb3Jj Invítame un café: ko-fi.com/mindmachinetv ====================================================== Redes Sociales: Twitter: https://goo.gl/LNyICo Facebook:https://goo.gl/lcb4Ab Instagram: https://goo.gl/fmLa4J ====================================================== Fuentes que hicieron posible este video: Data mining concepts and techniques: http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf ====================================================== Programas que utilizo: Adobe after effects Adobe illustrator Ableton Live 9 Equipo que utilizo: Huion 680s Audio-Technica ATR2500-USB ====================================================== Musica: https://soundcloud.com/musicadfondo Descarga fondos: http://mindmachinetv.tumblr.com/
Views: 5402 MindMachineTV
Jiawei Han receives 2009 W. Wallace McDowell Award
 
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The IEEE Computer Society presented its 2009 W. Wallace McDowell Award to Jiawei Han for significant contributions to knowledge discovery and data mining. The W. Wallace McDowell Award honors the outstanding recent theoretical, design, educational, practical, innovative contributions within the computing field. Dr. Han accepted his award at the Computer Society's 25 May 2011 awards ceremony in Albuquerque, New Mexico. Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois. His research includes information network analysis, data mining, data warehousing, stream mining, text and Web mining, and software bug mining. Dr. Han was the first to introduce a compressed frequent pattern tree structure and a pattern-growth methodology for mining frequent, sequential, and structured patterns. The "FP-Tree" is still the fastest method to do association rules - one of the most influential concepts in the last 15 years of data mining. For more information about Jiawei Han: http://www.computer.org/portal/web/awards/Jiawei-Han For more information about IEEE Computer Society Awards: http://www.computer.org/awards
Views: 1938 ieeeComputerSociety
Knowledge Mining in Heterogeneous Information Networks - Jiawei Han
 
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Summer School in cognitive Science: Web Science and the Mind Institut des sciences cognitives, UQAM, Montréal, Canada http://www.summer14.isc.uqam.ca/ http://www.isc.uqam.ca/ JIAWEI HAN, University of Illinois at Urbana-Champaign, Department of Computer Science Knowledge Mining in Heterogeneous Information Networks OVERVIEW: People and informational objects are interconnected, forming gigantic, interconnected, integrated information networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, semi-structured, heterogeneous information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective construction, exploration and analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this talk, we present principles, methodologies and algorithms for mining in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a promising research frontier in data mining research. Departing from many existing network models that view data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data. This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining and exploring interconnected data, such as rank-based clustering and classification, meta path-based similarity search, and meta path-based link/relationship prediction. We will also discuss our recent progress on construction of quality semi-structured heterogeneous information networks from unstructured data and point out some promising research directions.
Association Rule Mining (Arabic)- A.B.O.L.K.O.G
 
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Lesson 2: Using Clementine for Association Rule Mining Related: Introduction : http://www.youtube.com/watch?v=mG1ef9fZ-kk For more visit us: http://www.abolkog.com
Views: 10640 Khalid Elshafie
Data analysis on soccer team performance
 
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Group 201512-18 Rui Wang Shuaiyu Han
Views: 1486 Ray
DataMining12-L13 : Frequent Items (1 of 3)
 
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Video Lectures by Prof. Jeff M. Phillips given as courses in the School of Computing at the University of Utah. Topics include Data Mining, Computational Geometry, and Big Data Algorithmics.
Views: 1210 Jeff Phillips
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies Please write back to us at [email protected] or call us at +918880862004 or 18002759730 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 55300 edureka!
I Dread Data Mining & Warframe Drama
 
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Many words are being spoken about Datamining, Warframe and Void_Glitch, though very little is actually being said. I want to clarify what Datamining actually is so we can move on from this dreadful situation. ****************************** References: 1. http://webcache.googleusercontent.com/search?q=cache:i3-XBI5RnKMJ:https://github.com/VoiDGlitch/WarframeData/blob/master/MissionDecks.txt&num=1&hl=fr&strip=0&vwsrc=0 2. https://docs.google.com/document/d/1ajTaNflzEj3lplS4htrHbBTdOqAlhPXiXtH1cDxWB8M/mobilebasic?pli=1 3. https://www.reddit.com/r/Warframe/comments/3sleik/leaked_primed_mods/ 4. https://www.reddit.com/r/Warframe/comments/6an10t/images_of_umbra_excal_cause_the_post_was_deleted/ 5. https://forums.warframe.com/topic/808245-drop-rates-datamines-and-digital-extremes-ddd/ - https://imgur.com/a/RTzVl 6. https://www.reddit.com/r/Warframe/comments/6j70z7/quick_question_about_data_mining_laws/ 7. https://www.reddit.com/r/Warframe/comments/6j248z/open_letter_to_derebecca/ 8. Han, Jiawei, Micheline Kamber & Jian Pei. "Data Mining: Concepts and Techniques." Elsevier. 2012. P. XXIII (https://books.google.nl/books?hl=nl&lr=&id=pQws07tdpjoC&oi=fnd&pg=PP1&dq=data+mining+twitter&ots=tyMyZ-kCVW&sig=JcB_MjFaI_2-0DrERCFolAVT2vg#v=onepage&q=data%20mining%20twitter&f=false) 9. Fayyad Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth. "From Data Mining to Knowledge Discovery in Databases". AI Magazine 17.3:(1996) p. 43 10. https://forums.warframe.com/topic/808245-drop-rates-datamines-and-digital-extremes-ddd/?page=10#comment-8773094 ***************** Welcome to the "I Dread" series, where I talk about everything I dread within Warframe. Does this mean I don't like the game? Actually it's the exact opposite, but I don't like dreadful things in it. ****************************** Make Sure you Follow Me on: Twitter: https://www.twitter.com/inglriousb Twitch: https://www.twitch.tv/TNL_official Join the Discord: https://discord.gg/XNYEJhV ************************* Want to start Warframe with a booster whilst supporting me at the same time? Click the Referral link below and start off with a 7 day affinity booster. https://www.warframe.com/signup?referrerId=519728441a4d806e71000033 ************************* Credits: Kevin MacLeod - "Killing Time" Kevin MacLeod - "Wallpaper" Kevin MacLeod - "Cold Sober" The footage and images featured in the video are for critical review and parody. Video footage is credited in the video. Stalker Model by Coverop ************************* Glyph Code PC: 837E-27F5-FDD5-7896 Glyph Code PS4: 0736-AA13-87EC-232A Glyph Code XB1: B4B4-92A2-ECEB-EA2C Redeem your TNL Glyph on https://www.warframe.com/promocode. Find out how to get your own TNL glyph in this video: https://www.youtube.com/watch?v=v4FCdaWn0Io
Views: 737 Michel Postma
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
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The FP-Growth Algorithm, proposed by Han, is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
Views: 66141 StudyKorner
Data Mining & Business Intelligence | Tutorial #20 | Data Reduction - Concept Hierarchy Generation
 
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Order my books at 👉 http://www.tek97.com/ Confused about what is Concept Hierarchy Generation in Data Mining in the context of Data Mining well this video is for you. Watch Now! ارتباك حول ما هو الجيل الهرمي مفهوم في "التعدين البيانات" في سياق "التنقيب عن البيانات" جيدا هذا الفيديو لك. شاهد الآن! Confus au sujet de ce qu'est la génération de hiérarchie de concept dans l'exploration de données dans le contexte de l'exploration de données bien cette vidéo est pour vous. Regarde maintenant! Verwirrt darüber, was ist Konzept-Hierarchie-Generierung in Data Mining im Zusammenhang mit Data Mining gut dieses Video ist für Sie. Schau jetzt! Confundido sobre qué es la Generación de Jerarquía Conceptual en Minería de Datos en el contexto de la Minería de Datos, este video es para usted. ¡Ver ahora! Смутно о том, что представляет собой генерация концепции иерархии в интеллектуальном анализе данных в контексте Data Mining, это видео для вас. Смотри! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 778 Ranji Raj
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 152398 Well Academy
Bringing structure to text: mining phrases, entities, topics, and hie.. (KDD 2014)
 
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Bringing structure to text: mining phrases, entities, topics, and hierarchies KDD 2014 Jiawei Han Chi Wang Ahmed El-Kishky Mining phrases, entity concepts, topics, and hierarchies from massive text corpus is an essential problem in the age of big data. Text data in electronic forms are ubiquitous, ranging from scientific articles to social networks, enterprise logs, news articles, social media and general web pages. It is highly desirable but challenging to bring structure to unstructured text data, uncover underlying hierarchies, relationships, patterns and trends, and gain knowledge from such data. In this tutorial, we provide a comprehensive survey on the state-of-the art of data-driven methods that automatically mine phrases, extract and infer latent structures from text corpus, and construct multi-granularity topical groupings and hierarchies of the underlying themes. We study their principles, methodologies, algorithms and applications using several real datasets including research papers and news articles and demonstrate how these methods work and how the uncovered latent entity structures may help text understanding, knowledge discovery and management.
NYU optional video --- A project in Data Mining and its relevance to quantitative finance.
 
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Part of my application for the MSMF program at NYU. Author: Quan Wan
Views: 64 Quan Wan
Text Mining for Social Scientists
 
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Text mining refers to digital social research methods that involve the collection and analysis of unstructured textual data, generally from internet-based sources such as social media and digital archives. In this webinar, Gabe Ignatow and Rada Mihalcea discussed the fundamentals of text mining for social scientists, covering topics including research design, research ethics, Natural Language Processing, the intersection of text mining and text analysis, and tips on teaching text mining to social science students.
Views: 896 SAGE
Introduction to AI | Intro#1 | Artificial Intelligence in Eng-Hindi
 
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Chapter : Introduction to AI - AI(Artificial Intelligence) Text Books: 1. Stuart J. Russell and Peter Norvig, "Artificial Intelligence A Modern Approach “Second Edition" Pearson Education. 2. Saroj Kaushik “Artificial Intelligence” , Cengage Learning. 3. George F Luger “Artificial Intelligence” Low Price Edition , Pearson Education., Fourth edition. Reference Books: 1. Ivan Bratko “PROLOG Programming for Artificial Intelligence”, Pearson Education, Third Edition. 2. Elaine Rich and Kevin Knight “Artificial Intelligence” Third Edition 3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. 4. Hagan, Demuth, Beale, “Neural Network Design” CENGAGE Learning, India Edition. 5. Patrick Henry Winston , “Artificial Intelligence”, Addison-Wesley, Third Edition. 6. Han Kamber, “Data Mining Concepts and Techniques”, Morgann Kaufmann Publishers. 7. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press.
Views: 220 Minute Study
Agents in AI | Intro #2 | Artificial Intelligence in Eng-Hindi
 
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Chapter: Introduction to AI - AI(Artificial Intelligence) Text Books: 1. Stuart J. Russell and Peter Norvig, "Artificial Intelligence A Modern Approach “Second Edition" Pearson Education. 2. Saroj Kaushik “Artificial Intelligence” , Cengage Learning. 3. George F Luger “Artificial Intelligence” Low Price Edition , Pearson Education., Fourth edition. Reference Books: 1. Ivan Bratko “PROLOG Programming for Artificial Intelligence”, Pearson Education, Third Edition. 2. Elaine Rich and Kevin Knight “Artificial Intelligence” Third Edition 3. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. 4. Hagan, Demuth, Beale, “Neural Network Design” CENGAGE Learning, India Edition. 5. Patrick Henry Winston , “Artificial Intelligence”, Addison-Wesley, Third Edition. 6. Han Kamber, “Data Mining Concepts and Techniques”, Morgann Kaufmann Publishers. 7. N.P.Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press.
Views: 132 Minute Study
(NET303) Facebook, Data and Politics Policy Primer
 
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[1] Statista. (2018). Number of monthly active users worldwide as of 2nd quarter 2018 (in millions). Retrieved from https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ [2] Statista. (2018). Most popular social networks worldwide as of October 2018, ranked by number of active users (in millions). Retrieved from https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ [3] Pearce, D. (2013). What does Facebook know about you. Retrieved from http://daylandoes.com/facebook-and-your-data/ [4] Elison, N. B., Vitak, J., Steinfield, C., Gray, R., & Lampe, C. (2011). Negotiating privacy concerns and social capital needs in a social media environment. In S. Trepte & L. Reinecke (Eds), Privacy online (pp. 19-32). Hiedelberg, DE: Springer. [5] Misra, G., & Such, J. M. (2016). How socially aware are social media privacy controls?. Computer, 49, 96-99. doi: 10.1109/MC.2016.83 [6] Furini, M., & Tamanini, V. (2015). Location privacy and public metadata in social media platforms: attitudes, behaviors and opinions. Multimedia Tools and Applications, 74, 9795-9825. doi: 10.1007/s11042-014-2151-7 [7] Markovij, D., Gievska, S., Kosinski, M., & Stillwell, D. (2013). Mining Facebook data for predictive personality modeling. Proceedings Of AAAI International Conference on Weblogs and Social Media, 23-26. [8] Bechman, A. (2014). Non-informed consent cultures: Privacy policies and app contracts on Facebook. Journal of Media Business Studies, 11, 21-38. https://doi.org/10.1080/16522354.2014.11073574 [9] Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Burlington, US: Morgan Kaufmann. [10] Facebook. (2018). Data Policy. Retrieved from https://www.facebook.com/policy.php [11] Katsikas, S. K., & Zorkadis, V. (2017). E-democracy – privacy-preserving, secure, intelligent e-government services. Communications in Computer and Information Science, 792, 1-276. https://doi.org/10.1007/978-3-319-71117-1
Views: 19 Nath B
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
 
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This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1848646 Khan Academy
Data Mining & Business Intelligence | Tutorial #12 | Data Integration Process
 
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Order my books at 👉 http://www.tek97.com/ Lets see what is Data Integration and its issues in various spheres of Data Mining. Watch now ! لنرى ما هو التكامل البيانات وقضاياها في مختلف مجالات البيانات التنقيب. شاهد الآن ! Давайте посмотрим, что такое Интеграция данных и ее проблемы в различных областях интеллектуального анализа данных. Смотри ! Voyons ce qu'est l'intégration de données et ses problèmes dans diverses sphères de l'exploration de données. Regarde maintenant ! Sehen wir uns an, was Data Integration und ihre Probleme in verschiedenen Bereichen des Data Mining sind. Schau jetzt ! Veamos qué es la integración de datos y sus problemas en varias esferas de la minería de datos. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 1176 Ranji Raj
Data Mining & Business Intelligence | Tutorial #19 | Data Discretization
 
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Order my books at 👉 http://www.tek97.com/ Learn what is data discretization in data reduction in context of data mining. Watch now ! تعرف على ما هو تقديس البيانات في الحد من البيانات في سياق استخراج البيانات. شاهد الآن ! Aprenda qué es la discretización de datos en la reducción de datos en el contexto de la minería de datos. Ver ahora ! Узнайте, что такое дискретизация данных при сокращении данных в контексте интеллектуального анализа данных. Смотри ! Erfahren Sie, was Datendiskretisierung bei der Datenreduktion im Kontext von Data Mining ist. Schau jetzt ! Apprenez ce qu'est la discrétisation des données dans la réduction des données dans le contexte de l'exploration de données. Regarde maintenant ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 1079 Ranji Raj
Data Mining & Business Intelligence | Tutorial #13 | Data Transformation Process
 
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Order my books at 👉 http://www.tek97.com/ Interested to know about how data is transformed in Data Mining well this video is the answer for that! Watch now ! مهتم بمعرفة كيف يتم تحويل البيانات في Data Mining بشكل جيد هذا الفيديو هو الحل لذلك! شاهد الآن ! Interesado en saber cómo se transforman los datos en Data Mining, este video es la respuesta para eso. Ver ahora ! Interessiert zu wissen, wie Daten in Data Mining umgewandelt werden, ist dieses Video die Antwort dafür! Schau jetzt ! Intéressé de savoir comment les données sont transformées dans Data Mining bien cette vidéo est la réponse pour cela! Regarde maintenant ! Заинтересованы в том, как данные преобразуются в Data Mining, и это видео является ответом на это! Смотри ! Interesado en saber cómo se transforman los datos en Data Mining, este video es la respuesta para eso. Ver ahora ! ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Add me on Facebook 👉https://www.facebook.com/renji.nair.09 Follow me on Twitter👉https://twitter.com/iamRanjiRaj Read my Story👉https://www.linkedin.com/pulse/engineering-my-quadrennial-trek-ranji-raj-nair Visit my Profile👉https://www.linkedin.com/in/reng99/ Like TheStudyBeast on Facebook👉https://www.facebook.com/thestudybeast/ ⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ For more such videos LIKE SHARE SUBSCRIBE Iphone 6s : http://amzn.to/2eyU8zi Gorilla Pod : http://amzn.to/2gAdVPq White Board : http://amzn.to/2euGJ7F Duster : http://amzn.to/2ev0qvX Feltip Markers : http://amzn.to/2eutbZC
Views: 897 Ranji Raj
Learning from Bacteria about Social Networks
 
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Google Tech Talk (more info below) September 30, 2011 Presented by Eshel Ben-Jacob. ABSTRACT Scientific American placed Professor Eshel Ben-Jacob and Dr. Itay Baruchi's creation of a type of organic memory chip on its list of the year's 50 most significant scientific discoveries in 2007. For the last decade, he has pioneered the field of Systems Neuroscience, focusing first on investigations of living neural networks outside the brain. http://en.wikipedia.org/wiki/Eshel_Ben-Jacob Learning from Bacteria about Information Processing Bacteria, the first and most fundamental of all organisms, lead rich social life in complex hierarchical communities. Collectively, they gather information from the environment, learn from past experience, and make decisions. Bacteria do not store genetically all the information required to respond efficiently to all possible environmental conditions. Instead, to solve new encountered problems (challenges) posed by the environment, they first assess the problem via collective sensing, then recall stored information of past experience and finally execute distributed information processing of the 109-12 bacteria in the colony, thus turning the colony into super-brain. Super-brain, because the billions of bacteria in the colony use sophisticated communication strategies to link the intracellular computation networks of each bacterium (including signaling path ways of billions of molecules) into a network of networks. I will show illuminating movies of swarming intelligence of live bacteria in which they solve optimization problems for collective decision making that are beyond what we, human beings, can solve with our most powerful computers. I will discuss the special nature of bacteria computational principles in comparison to our Turing Algorithm computational principles, showing that we can learn from the bacteria about our brain, in particular about the crucial role of the neglected other side of the brain, distributed information processing of the astrocytes. Eshel Ben-Jacob is Professor of Physics of Complex Systems and holds the Maguy-Glass Chair in Physics at Tel Aviv University. He was an early leader in the study of bacterial colonies as the key to understanding larger biological systems. He maintains that the essence of cognition is rooted in the ability of bacteria to gather, measure, and process information, and to adapt in response. For the last decade, he has pioneered the field of Systems Neuroscience, focusing first on investigations of living neural networks outside the brain and later on analysis of actual brain activity. In 2007, Scientific American selected Ben-Jacob's invention, the first hybrid NeuroMemory Chip, as one of the 50 most important achievements in all fields of science and technology for that year. The NeuroMemory Chip entails imprinting multiple memories, based upon development of a novel, system-level analysis of neural network activity (inspired by concepts from statistical physics and quantum mechanics), ideas about distributed information processing (inspired by his research on collective behaviors of bacteria) and new experimental methods based on nanotechnology (carbon nanotubes). Prof. Ben-Jacob received his PhD in physics (1982) at Tel Aviv University, Israel. He served as Vice President of the Israel Physical Society (1999-2002), then as President of the Israel Physical Society (2002-2005), initiating the online magazine PhysicaPlus, the only Hebrew-English bilingual science magazine. The general principles he has uncovered have been examined in a wide range of disciplines, including their application to amoeboid navigation, bacterial colony competition, cell motility, epilepsy, gene networks, genome sequence of pattern-forming bacteria, network theory analysis of the immune system, neural networks, search, and stock market volatility and collapse. He has examined implications of bacterial collective intelligence for neurocomputing. His scientific findings have prompted studies of their implications for computing: using chemical "tweets" to communicate, millions of bacteria self-organize to form colonies that collaborate to feed and defend themselves, as in a sophisticated social network. This talk was hosted by Boris Debic, and arranged by Zann Gill and the Microbes Mind Forum.
Views: 27671 GoogleTechTalks
Clustering data (and machine learning) (3-3b)
 
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This video is part of the Analyzing and Visualizing Data with Power BI course available on EdX.  To sign up for the course, visit: http://aka.ms/pbicourse. To read more: Power BI service https://aka.ms/pbis_gs Power BI Desktop https://aka.ms/pbid_gs Power BI basic concepts tutorial: https://aka.ms/power-bi-tutorial To submit questions and comments about Power BI, please visit community.powerbi.com. To submit questions and comments about Power BI, please visit community.powerbi.com.
Views: 19474 Microsoft Power BI
Data Warehousing Tutorial - 1 | Data Warehousing Tutorial for Beginners - 1 | Edureka
 
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***** Data Warehouse & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** A data warehouse is a central location where consolidated data from multiple locations are stored. It usually contains historical data derived from transaction data but it can include data from other sources. Topics covered in the Video: 1.What is Datawarehouse? 2.Data warehouse Architecture 3.Why Data warehouse is used? 4.What is ETL? 5.What all you will learn in Data warehousing and ETL course? 6.Hands on Watch the sample class recording: http://www.edureka.co/data-warehousing-and-bi?utm_source=youtube&utm_medium=referral&utm_campaign=datawarehouse Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Introduction to Dataware Housing’ have been covered in our course ‘Datawarehousing‘. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004"
Views: 208429 edureka!
Recent Advances in Feature Selection: A Data Perspective part 2
 
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Authors: Huan Liu, Department of Computer Science and Engineering, Arizona State University Jundong Li, School of Computing, Informatics and Decision Systems Engineering, Arizona State University Jiliang Tang, Department of Computer Science and Engineering, Michigan State University Abstract: Feature selection, as a data preprocessing strategy, is imperative in preparing high-dimensional data for myriad of data mining and machine learning tasks. By selecting a subset of features of high quality, feature selection can help build simpler and more comprehensive models, improve data mining performance, and prepare clean and understandable data. The proliferation of big data in recent years has presented substantial challenges and opportunities for feature selection research. In this tutorial, we provide a comprehensive overview of recent advances in feature selection research from a data perspective. After we introduce some basic concepts, we review state-of-the-art feature selection algorithms and recent techniques of feature selection for structured, social, heterogeneous, and streaming data. In particular, we also discuss what the role of feature selection is in the context of deep learning and how feature selection is related to feature engineering. To facilitate and promote the research in this community, we present an open-source feature selection repository scikit-feature that consists of most of the popular feature selection algorithms. We conclude our discussion with some open problems and pressing issues in future research. Link to tutorial: http://www.public.asu.edu/~jundongl/tutorial/KDD17/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 432 KDD2017 video
Apriori algorithm with complete solved example to find association rules
 
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Complete description of Apriori algorithm is provided with a good example. Apriori is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
Views: 21055 StudyKorner
Understand the Blockchain in Two Minutes
 
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Over the past decade, an alternative digital paradigm has slowly been taking shape at the edges of the internet. This new paradigm is the blockchain. After incubating through millions of Bitcoin transactions and a host of developer projects, it is now on the tips of tongues of CEOs and CTOs, startup entrepreneurs, and even governance activists. Though these stakeholders are beginning to understand the disruptive potential of blockchain technology and are experimenting with its most promising applications, few have asked a more fundamental question: What will a world driven by blockchains look like a decade from now? Learn more: http://www.iftf.org/blockchainfutureslab Contact us: http://www.iftf.org/blockchainfutureslab/contact
Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks
 
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Authors: Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, Jiawei Han Abstract: One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features. ACM DL: http://dl.acm.org/citation.cfm?id=2783374 DOI: http://dx.doi.org/10.1145/2783258.2783374
Association Rule Mining projects
 
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Views: 65 PHD Projects
Openwest 2015 - Jim Lohse - "Hadoop, MapReduce, Weka & Python Pandas, Oh My?" (68)
 
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From old school Java-based toolkits like Weka to the latest and greatest toolkit for machine learning like Python Pandas, here's what you need to know for an introduction to the world of Big Data and Machine Learning. Geared towards students and professionals who need to understand the basics of this topic, we will present various concepts from cluster-based file systems, "Not Only SQL" Databases, Map Reduce algorithms and the range of tools for machine learning and data mining including Weka, Python Pandas, R and many other Python toolkits. Thursday, May 7th, 04:00pm-04:45pm Room LA 101 (Data)
Views: 352 Utah Open Source
hazards in mining and possible solutions pdf
 
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Contact Us For Help: http://wwa.stonecrushersolution.org/solutions/solutions.html A Guide to Office Safety and Health, N.C. Department of Labor A Guide to Office Safety and Health examines many of these potential risks and offers solutions to them. General office safety hazards do exist in the Hazards Lesson 19: Ergonomics, American Safety Council Workplace analysis is the best way to identify existing and potential workplace hazards and discover ways to correct these hazards. Assessment of work tasks Woodworking eTool: Safety Hazards Fire and Explosion Woodworking eTool: Safety Hazards Fire and Explosion http://www.osha.gov/SLTC/etools/woodworking/commonhaz_fireexplosion.html[8/17/2011 6:05:38 PM] 1. Introduction explain the causes of natural pheonomena 1. Introduction Due to the lack of scientific knowledge in the ancient society, people were unable to explain the causes of natural pheonomena, such as storms Han and Kamber: Data Mining, , , Concepts and Techniques, 2nd ed “We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. This Third Edition Job Safety Analysis, Ohio BWC JOB SAFETY ANALYSIS JSA Benefits Prevention/reduction of hazards in the performance of your job Prevention/reduction of Occupational Hazards in Home Health Care Patio Hazards Occupational Hazards in Home Health Care Possible Solutions ? Implement a written program which meets the requirements of the Hazard Communication Standard to Reducing Employee Slips, Trips and Falls Michigan Municipal Workers’ Compensation Fund Safety and Health Resource Manual Reducing Employee Slips, Trips and Falls The Problem Work place falls injure or kill National Library of Canada Cataloguing in Publication Data National Library of Canada Cataloguing in Publication Data problems and possible solutions to help save 5 Where possible, map the location of hazards, Mine Safety, Audit Report Roa Mine, Department of Labour 3. Background to Roa Mine. Roa mine is owned and operated by Roa Mining Co Ltd and consists of two underground operations and one surface operation. U.S. Geological Survey: Natural Hazards Natural Hazards information from the U.S. Geological Survey (USGS). Health Hazards, Sciencecorps Health Hazards of Chemicals. Commonly Used on Military Bases . The chemical exposures of men, women, and children on military bases in the US are well, established. SHOP SAFETY, University of Texas at Arlington Page 4, 6 Ladders Ladders can make many tasks easier, but they are also a continual safety hazard. Even the best ladder is not safe unless you are trained and Construction challenges Practical guide and resources 3. Identify the resources the committee requires (internal and external). Zurich will assist in gathering these individuals or groups for possible future A PRACTICAL URBAN PLANNING PERSPECTIVE OF THE QUEENSLAND FLOODS 1 4 April 2011 . A PRACTICAL URBAN PLANNING PERSPECTIVE OF THE QUEENSLAND FLOODS . Introduction: Since the most recent drought which began in 2006/2007, there has The main cause of global warming, Time for change A major cause of global warming is the attitude of mankind to Nature. Technical solutions alone won't be enough to fight global warming, we have to wake up and change In Situ Leach Mining (ISL) of Uranium solution mining, Safety in Uranium Mining; to 150% of their design operating pressure to ensure no leakage to overlying aquifers is possible. Home Hazards and ZME Safety Guide & Tips Home Hazards and ZME Safety Guide & Tips. By: MortgageLoan.com (Multiple Contributors) Updated and reviewed: April 23, 2013. Please note that you are free to CROETweb: Maritime Safety and Health — Hazards //www.osha.gov/Publications/OSHA3654.pdf Abrasive Blasting Hazards in Shipyard What are the hazards and possible solutions Working Alone, WorkSafeBC ? Working Alone Do you have employees who are assigned to work alone or in isolation? If the answer is “yes,” you must do the following: ? Identify hazards CHAPTER 1, INCORPORATING NATURAL HAZARD MANAGEMENT INTO THE This chapter defines natural hazards and their is only possible to the degree tourism sector to natural hazards, for example, solutions were proposed Hazards in the workplace: resources — National Union of Workers Hazards in the workplace: resources Body mapping discuss these with other members to help generate possible solutions to these problems. (pdf) Hot and cold What Effects Can the Environment Have on Health? Physical Hazards, and their Adverse Health Effects Although you will have heard or read a great deal about the environmental consequences of global warming, man will Health
Views: 170 rxlp qloga
Data Mining with Weka (3.4: Decision trees)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 4: Decision trees http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 65591 WekaMOOC
Kylin Analytics, OLAP for Big Data
 
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Kylin Analytics big performance demonstration
Views: 1183 Stratebi
Text mining highlight talk: ISMB 2014
 
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ISMB Highlight talk by Ashutosh Malhotra:Linking hypothetical patterns to disease molecular signatures in Alzheimer's disease
Views: 589 atmbio
PhD Public Defence - Tingting Liu
 
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Demystification of Hidden Markov Models: on Identifiability, Learnability and Applicability Hidden Markov Models (HMMs) applied in machine learning and data mining fields has been an active research topic in computer science over the past decades, ranging from theoretical to very practical. Recognized by its competitive advantages in temporal pattern recognition, HMMs have been widely and successfully applied in diverse application fields such as speech recognition, hand-writing and text recognition, biosciences, machine maintenance and image processing, etc. Despite their success, a deep understanding is still lacking. HMMs are being used as black boxes, without understanding which parameter configurations make models unique. Such models cannot be mimicked by other models and therefore are the ones we should be able to learn from data. Despite some theoretical barriers, there has been significant process on the practical side, albeit often without a sound understanding about the underlying mathematical principles. This is a common problem of black-box solutions with most of the state-of-the-art statistical modelling techniques. This dissertation contributes to this research domain by introducing numerous novel concepts that helps to better understand the theory of the identification of HMMs. Based on an analysis of how information passes through the model, deep insights are provided to demystify HMMs with respect to identifiability and learnability. Apart from the progress on the theoretical side, we propose a novel initialization approach for the traditional Baum-Welch (BW) algorithm, called the Segmentation-Clustering and Transient (SCT) based learning method, that improves both the efficiency and the effectiveness in learning HMMs compared to the classical BW approach which often uses random guess of model parameters. Finally, a validation framework is proposed to evaluate the reliability and robustness of predictors built on the learned models. Empirical assessments conducted in this dissertation indicate that the presented research offers a better understanding in HMMs as well as providing competitive results compared to prior state-of-the-art work.
Views: 24 Tingting LIU
Battlefront 2 - 5 Things You SHOULDN'T Be Seeing #2
 
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SUBMIT YOUR BATTLEFRONT II CLIPS! ➵https://docs.google.com/forms/d/1tUdEZP_azvPahzVMy5rE21P40u_2N9OcOafh3Z3tFOQ/edit Thanks for watching! Music provided by Epidemic Sound Songs (in order): ➵ES_The Balearic Dreamer 2 ➵ES_Aiming High 5 Outro song: ➵Fleetwood Mac - The Chain (GOTG2 Remix)
Views: 20355 Harrison James
Cognitive Sciences Applications in Big Data (WMSCI 2014)
 
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"Cognitive Sciences Applications in Big Data" (General Joint Session at WMSCI 2014) Dr. Leonid Perlovsky Harvard University and The Air Force Research Laboratory, USA Abstract: Big Data problems have been efficiently addressed with cognitive algorithms modeling mechanisms of the mind. The talk describes cognitive algorithms, their applications to various engineering problems, including Big Data, and their foundations in mathematical models of the mind including higher cognitive abilities. Mechanisms of the mind include concepts, emotions, hierarchy, dynamic logic, and interaction between language and cognition. Big Data analytics requires algorithms modeling all these abilities. Machine learning, artificial intelligence, and modeling of the mind has been plagued by computational complexity since the 1960s. Dynamic logic overcomes computational complexity when analyzing Big Data. It is a process-logic, which replaces classical logic; it serves as a basis for cognitive algorithms and for a mathematical theory of learning, combining the mechanisms of the mind into a hierarchical system of mental processes. Each process proceeds "from vague to crisp," from vague representation-concepts to crisp ones. Brain imaging experiments (Bar et al 2006; Kveraga et al 2007) confirmed this as an adequate model of the brain perception and cognition. Computational difficulty is related to Gödelian problems in logic: computational complexity is a manifestation of Gödelian incompleteness in finite systems, such as computers or brains. The mind is "not logical." Dynamic logic overcomes this difficulty. Engineering applications demonstrate orders of magnitude improvement in Big Data analytics, data mining, information integration, financial predictions, genetic studies, cybersecurity. The talk presents the dual hierarchy model of interactions between language and cognition. It enables integrating language, text, and sensor data. A number of "mysteries" in this interaction are explained: what is the difference between them; what is the role of language in cognition, why children can talk before they really understand, how much adults are different from children in this respect, etc. These are explained in the model, and explanations are confirmed in brain imaging experiments (Binder et al 2005; Price 2012). Much difficulties in developing Big Data algorithms are related to confusing language and cognition. The knowledge instinct drives acquisition of cognitive ability and is a foundation of all our higher cognitive abilities. Its satisfaction is experienced as aesthetic emotions (experimentally confirmed in Cabanac et al 2010). Efficient engineering algorithms must model these emotional abilities (Perlovsky, Deming, Ilin, 2011). The hierarchy of aesthetic emotions is discussed from understanding of everyday objects, to understanding of abstract concepts throughout the hierarchy, to the near top of the mental hierarchy. Contents of these "highest" concepts are discussed and the corresponding aesthetic emotions are related to the beautiful. Experimental tests of this conjecture are for the near future. Contradictions among knowledge are experienced as negative aesthetic emotions, cognitive dissonance. Development of robots and human-computer interactions require algorithms modeling this ability. Cognitive dissonance counteracts the knowledge instinct and would prevent accumulation of knowledge and the entire human evolution, if not a special ability evolved for overcoming these emotions. It follows from the dual hierarchy model that this mechanism is music. This theoretical prediction has been experimentally confirmed (Masataka et al 2012, 2013, Cabanac et al, 2013). This explains the origin and evolution of music, what Darwin called the greatest mystery.
Views: 406 IIISchannel
Recent Advances in Feature Selection: A Data Perspective part 3
 
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Authors: Huan Liu, Department of Computer Science and Engineering, Arizona State University Jundong Li, School of Computing, Informatics and Decision Systems Engineering, Arizona State University Jiliang Tang, Department of Computer Science and Engineering, Michigan State University Abstract: Feature selection, as a data preprocessing strategy, is imperative in preparing high-dimensional data for myriad of data mining and machine learning tasks. By selecting a subset of features of high quality, feature selection can help build simpler and more comprehensive models, improve data mining performance, and prepare clean and understandable data. The proliferation of big data in recent years has presented substantial challenges and opportunities for feature selection research. In this tutorial, we provide a comprehensive overview of recent advances in feature selection research from a data perspective. After we introduce some basic concepts, we review state-of-the-art feature selection algorithms and recent techniques of feature selection for structured, social, heterogeneous, and streaming data. In particular, we also discuss what the role of feature selection is in the context of deep learning and how feature selection is related to feature engineering. To facilitate and promote the research in this community, we present an open-source feature selection repository scikit-feature that consists of most of the popular feature selection algorithms. We conclude our discussion with some open problems and pressing issues in future research. Link to tutorial: http://www.public.asu.edu/~jundongl/tutorial/KDD17/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 202 KDD2017 video
How does a blockchain work - Simply Explained
 
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What is a blockchain and how do they work? I'll explain why blockchains are so special in simple and plain English! 💰 Want to buy Bitcoin or Ethereum? Buy for $100 and get $10 free (through my affiliate link): https://www.coinbase.com/join/59284524822a3d0b19e11134 📚 Sources can be found on my website: https://www.savjee.be/videos/simply-explained/how-does-a-blockchain-work/ 🐦 Follow me on Twitter: https://twitter.com/savjee ✏️ Check out my blog: https://www.savjee.be ✉️ Subscribe to newsletter: https://goo.gl/nueDfz 👍🏻 Like my Facebook page: https://www.facebook.com/savjee
Views: 2291243 Simply Explained - Savjee

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