Home
Search results “Han data mining concepts”
Jiawei Han
 
01:19:26
Views: 1574 SonicNU
Lecture - 34 Data Mining and Knowledge Discovery
 
54:46
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 132592 nptelhrd
Philip Evans: How data will transform business
 
13:58
What does the future of business look like? In an informative talk, Philip Evans gives a quick primer on two long-standing theories in strategy — and explains why he thinks they are essentially invalid. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 219485 TED
Association Rule Mining (Arabic)- A.B.O.L.K.O.G
 
10:00
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: 10471 Khalid Elshafie
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
13:19
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: 133366 Well Academy
Jiawei Han receives 2009 W. Wallace McDowell Award
 
04:01
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: 1921 ieeeComputerSociety
From High Dimensional Data to Big Data - Han Liu
 
50:18
Han Liu Princeton University February 27, 2014 We introduce a new family of robust semiparametric methods for analyzing large, complex, and noisy datasets. Our method is based on the transelliptical distribution family which assumes that the variables follow an elliptical distribution after a set of unknown marginal transformations. The transelliptical family includes many existing distributions as special cases and can be used to robustify a wide range of multivariate methods, including sparse covariance matrix estimation, principal component analysis, graphical models, discriminant analysis, regression analysis, and principal component regression. We present a hierarchical representation of the transelliptical distribution and propose a new estimation technique based on robust rank correlations. The theoretical properties of the transelliptical methods rely on the concentration behavior of a new type of random matrices under different norms. I will lay out some existing results and introduce an remaining open problem. This talk is based on joint work with Fang Han. For more videos, visit http://video.ias.edu
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
 
24:46
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: 51570 StudyKorner
Knowledge Mining in Heterogeneous Information Networks - Jiawei Han
 
48:55
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.
NYU optional video --- A project in Data Mining and its relevance to quantitative finance.
 
05:01
Part of my application for the MSMF program at NYU. Author: Quan Wan
Views: 64 Quan Wan
Recent Advances in Feature Selection: A Data Perspective part 3
 
01:12:29
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: 173 KDD2017 video
Recent Advances in Feature Selection: A Data Perspective part 2
 
01:04:31
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: 378 KDD2017 video
¿Qué es la minería de datos?
 
04:28
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: 3376 MindMachineTV
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
50:19
( 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: 49141 edureka!
Data Mining & Business Intelligence | Tutorial #12 | Data Integration Process
 
07:23
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: 644 Ranji Raj
CS 201 PROF JIAWEI HAN
 
01:23:02
Views: 713 Edna Todd
“Halalchain as empowered by Blockchain technology” Mr. Abdullah Han Interview with CNBC Arabiyyah on
 
05:57
Mr. Abdullah Han Interview with CNBC Arabiyyah on "Halalchain as empowered by Blockchain technology". The leading Blockchain company HLC Technologies Co-Founder & Managing Director, Mr. Abdullah Han, held an interview with CNBC Arabiyyah on 13th February 2018. He has discussed the topic: "Halalchain as empowered by Blockchain technology". Mr. Abdullah explained the concept of Blockchain in Islamic economics that, is one of the latest underlying technologies based on Directed Acyclic Graph which has been defined as Blockchain 3.0 technology. The DAG involves no mining, supports quick transactions, and is friendly to small payment. Finally, Mr. Abdullah answered the question on Halalchain technologies that, it is at the forefront of the Blockchain industry, providing comprehensive solutions to Halal industry traceability, Islamic Economics and Finance, instant E payment, and other industry applications powered by blockchain, Internet of things, big data, Artificial Intelligence, biometrics technologies. Both Co-Founder Prof. Abdussalam and Prof. Yousuf, the Chairman, Dubai Institute of Blockchain committee attended the interview at CNBC Arabiyyah, Dubai Media City, UAE.
Views: 311 HalalChain
Solving Data Analytics Problems with Delite
 
44:16
In Scala 2.11 we introduced quasiquotes, a rich domain-specific api for tree manipulation. Despite advanced functionality and deep integration into the language, the api itself is just a library that relies on existing language features: string interpolation, macros, type classes etc. This talk will walk through implementation of custom quotations for a tiny language embedded within Scala and introduce you to core concepts used in quasiquotes: splicing, cardinality, lifting/unlifting and show how all of this makes your life easier. As data sizes become larger and processor architectures become more parallel, programmers are often forced to resort to using low-level programming models in order to improve performance and scalability. The introduction of heterogeneous accelerators such as GPUs make the programmers' jobs even more difficult, as they must often combine code for multiple different programming models together in ad-hoc ways. Domain-specific languages (DSLs) offer an alternative approach, as high-level implicitly parallel domain abstractions can be transparently lowered to multiple heterogeneous architectures. We have developed a suite of data analytic DSLs for machine learning, graph analysis, and data manipulation that use the Delite framework in Scala to achieve high performance parallel execution across multiple processors, GPUs, and clusters of machines. This talk will describe how we can combine these DSLs together to build larger applications to solve complex, real-world problems in multiple domains with both higher programmer productivity and higher performance than alternative solutions. After introducing the infrastructure and DSLs themselves, the talk will provide a survey of some of the applications currently being developed by other research groups at Stanford using these DSLs and the resulting productivity and performance benefits. Each application represents an ongoing collaboration in areas such as computational biology, biomedical imaging, data mining, inference engines, and large scale graph analysis Author: Kevin Brown Kevin Brown is a PhD candidate at Stanford University. His research focuses on simplifying parallel programming using DSLs and compilers.
Views: 87 Parleys
2011-01-28 :: Data Mining :: Part 3
 
11:43
Webinar about Data Mining
Views: 45 lrrcenter
I Dread Data Mining & Warframe Drama
 
08:02
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: 733 Michel Postma
Data Science Methodology 101 - Data Understanding Concepts and Case Study
 
03:24
Enroll in the course for free at: https://bigdatauniversity.com/courses/data-science-methodology-2/ Data Science Methodology Grab you lab coat, beakers, and pocket calculator…wait what? wrong path! Fast forward and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed. Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. Learn the major steps involved in tackling a data science problem. Learn the major steps involved in practicing data science, with interesting real-world examples at each step: from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. https://bigdatauniversity.com/courses/data-science-methodology-2/
Views: 5996 Cognitive Class
Data mining tools
 
19:56
Views: 351 Prarinya Ekapho
Learning from Bacteria about Social Networks
 
01:04:56
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: 27538 GoogleTechTalks
Cognitive Sciences Applications in Big Data (WMSCI 2014)
 
41:34
"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: 403 IIISchannel
Introduction to AI | Intro#1 | Artificial Intelligence in Eng-Hindi
 
07:02
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: 172 Minute Study
التنقيب عن البيانات - Data Mining - part1
 
04:51
مثال تدريس على مادة Data Mining التنقيب عن البيانات شركة برادفورد تابعنا على الفيس بوك https://www.facebook.com/BRADFORDJOR تابعنا على الانستغرام https://www.instagram.com/bradford.jo/ الموقع الالكتروني https://bradford-jo.com/ اضفنا على سناب شات https://www.snapchat.com/add/bradfordcompany #برادفورد_للتدريب_اونلاين تابعنا على التويتر https://twitter.com/joBradford_jo
Data Mining with Weka (3.4: Decision trees)
 
09:30
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: 63451 WekaMOOC
Cryptocurrencies: Last Week Tonight with John Oliver (HBO)
 
25:21
Digital currencies are generating a lot of excitement. John Oliver enlists Keegan-Michael Key to get potential investors equally excited about the concept of caution. Connect with Last Week Tonight online... Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight
Views: 8312565 LastWeekTonight
Probability Theory - The Math of Intelligence #6
 
09:31
We'll build a Spam Detector using a machine learning model called a Naive Bayes Classifier! This is our first real dip into probability theory in the series; I'll talk about the types of probability, then we'll use Bayes Theorem to help us build our classifier. Code for this video: https://github.com/llSourcell/naive_bayes_classifier/ Hammad's Winning Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Principal%20Component%20Analysis Kristian's Runner up Code: https://github.com/kwichmann/PCA_and_autoencoders Please Subscribe! And like. And comment. That's what keeps me going. More Learning Resources: http://machinelearningmastery.com/naive-bayes-tutorial-for-machine-learning/ http://blog.datumbox.com/machine-learning-tutorial-the-naive-bayes-text-classifier/ http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/ https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/ https://www.youtube.com/watch?v=psHrcSacU9Y https://hackernoon.com/how-to-build-a-simple-spam-detecting-machine-learning-classifier-4471fe6b816e https://www.autonlab.org/tutorials/naive.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/
Views: 76441 Siraj Raval
You may be accidentally investing in cigarette companies | Bronwyn King
 
14:39
Tobacco causes more than seven million deaths every year -- and many of us are far more complicit in the problem than we realize. In a bold talk, oncologist Dr. Bronwyn King tells the story of how she uncovered the deep ties between the tobacco industry and the entire global finance sector, which invests our money in cigarette companies through big banks, insurers and pension funds. Learn how Dr. King has ignited a worldwide movement to create tobacco-free investments and how each of us can play a role in ending this epidemic. Check out more TED Talks: http://www.ted.com The TED Talks channel features the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED
Views: 67344 TED
Ever wonder how Bitcoin (and other cryptocurrencies) actually work?
 
26:21
Bitcoin explained from the viewpoint of inventing your own cryptocurrency. Videos like these made possible by patreon: https://patreon.com/3blue1brown Protocol Labs: https://protocol.ai/ Interested in contributing? https://protocol.ai/join/ Special thanks to the following patrons: http://3b1b.co/btc-thanks Some people have asked if this channel accepts contributions in cryptocurrency form as an alternative to Patreon. As you might guess, the answer is yes :). Here are the relevant addresses: ETH: 0x88Fd7a2e9e0E616a5610B8BE5d5090DC6Bd55c25 BTC: 1DV4dhXEVhGELmDnRppADyMcyZgGHnCNJ BCH: qrr82t07zzq5uqgek422s8wwf953jj25c53lqctlnw LTC: LNPY2HEWv8igGckwKrYPbh9yD28XH3sm32 Supplement video: https://youtu.be/S9JGmA5_unY Music by Vincent Rubinetti: https://soundcloud.com/vincerubinetti/heartbeat Here are a few other resources I'd recommend: Original Bitcoin paper: https://bitcoin.org/bitcoin.pdf Block explorer: https://blockexplorer.com/ Blog post by Michael Nielsen: https://goo.gl/BW1RV3 (This is particularly good for understanding the details of what transactions look like, which is something this video did not cover) Video by CuriousInventor: https://youtu.be/Lx9zgZCMqXE Video by Anders Brownworth: https://youtu.be/_160oMzblY8 Ethereum white paper: https://goo.gl/XXZddT Music by Vince Rubinetti: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 2167809 3Blue1Brown
Data analysis on soccer team performance
 
10:07
Group 201512-18 Rui Wang Shuaiyu Han
Views: 1380 Ray
Data Mining & Business Intelligence | Tutorial #20 | Data Reduction - Concept Hierarchy Generation
 
05:09
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: 367 Ranji Raj
Agents in AI | Intro #2 | Artificial Intelligence in Eng-Hindi
 
07:47
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: 96 Minute Study
Kylin.io OLAP Engine Open Course Video
 
01:06:44
Kylin.io OLAP Engine 公开课视频 - 小象学院 http://kylin.io
Views: 2661 Luke Han
DataMining12-L13 : Frequent Items (1 of 3)
 
37:47
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: 1209 Jeff Phillips
hazards in mining and possible solutions pdf
 
04:36
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: 166 rxlp qloga
Clustering data (and machine learning) (3-3b)
 
04:40
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: 18158 Microsoft Power BI
What is a Neural Network - Ep. 2 (Deep Learning SIMPLIFIED)
 
06:30
With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool! Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification, is the focus of our series. Classification involves taking a set of objects and some data features that describe them, and placing them into categories. This is done by a classifier which takes the data features as input and assigns a value (typically between 0 and 1) to each object; this is called firing or activation; a high score means one class and a low score means another. There are many different types of classifiers such as Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes. If you have used any of these tools before, which one is your favorite? Please comment. Neural nets are highly structured networks, and have three kinds of layers - an input, an output, and so called hidden layers, which refer to any layers between the input and the output layers. Each node (also called a neuron) in the hidden and output layers has a classifier. The input neurons first receive the data features of the object. After processing the data, they send their output to the first hidden layer. The hidden layer processes this output and sends the results to the next hidden layer. This continues until the data reaches the final output layer, where the output value determines the object's classification. This entire process is known as Forward Propagation, or Forward prop. The scores at the output layer determine which class a set of inputs belongs to. Links: Michael Nielsen's book - http://neuralnetworksanddeeplearning.com/ Andrew Ng Machine Learning - https://www.coursera.org/learn/machine-learning Andrew Ng Deep Learning - https://www.coursera.org/specializations/deep-learning Have you worked with neural nets before? If not, is this clear so far? Please comment. Neural nets are sometimes called a Multilayer Perceptron or MLP. This is a little confusing since the perceptron refers to one of the original neural networks, which had limited activation capabilities. However, the term has stuck - your typical vanilla neural net is referred to as an MLP. Before a neuron fires its output to the next neuron in the network, it must first process the input. To do so, it performs a basic calculation with the input and two other numbers, referred to as the weight and the bias. These two numbers are changed as the neural network is trained on a set of test samples. If the accuracy is low, the weight and bias numbers are tweaked slightly until the accuracy slowly improves. Once the neural network is properly trained, its accuracy can be as high as 95%. Credits: Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 364192 DeepLearning.TV
Dive Deep Into Your Data Pools
 
58:37
Numerous organizations already discovered Enterprise Linked Data as a powerful solution for a 360-degree view on various business objects. But how do they solve the big challenge of connecting their data pools in heterogeneous and highly dynamic information landscapes? Learn more about the manifold application scenarios of linked data and semantic technologies. Dive into your data pools to gain new insights and knowledge! More at: http://www.poolparty.biz/poolparty-5-1-comes-with-integrated-graph-search-feature/ Slides: http://de.slideshare.net/semwebcompany/dive-deep-into-your-data-pools
Apriori algorithm with complete solved example to find association rules
 
27:55
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: 16728 StudyKorner
GRIET-IT-2012-Efficient Frequent Pattern Mining
 
06:50
Project guide : K Prasanna Lakshmi Team Members : Krishna Teja T , Pradeep T , Ravi G , Shanthan Kumar N Description :CPS tree construction capturing frequent patterns. Technologies : Java Sockets,Java Applet
Views: 708 pradeep tallada
Data Warehousing Tutorial - 1 | Data Warehousing Tutorial for Beginners - 1 | Edureka
 
53:12
***** 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: 205525 edureka!