Search results “Explain constraint based association mining rule”
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 27010 Well Academy
Data Mining  Association Rule - Basic Concepts
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 26932 StudyKorner
Association Rule Mining – Solved Numerical Question on Apriori Algorithm(Hindi)
Association Rule Mining – Solved Numerical Question on Apriori Algorithm(Hindi) DataWarehouse and Data Mining Lectures in Hindi Solved Numerical Problem on Apriori Algorithm Data Mining Algorithm Solved Numerical in Hindi Machine Learning Algorithm Solved Numerical Problems in Hindi
Frequent Pattern (FP) growth Algorithm for Association Rule Mining
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
Last Minute Tutorials | Apriori algorithm | Association Rule Mining
NOTES:- Theory of computation : https://viden.io/knowledge/theory-of-computation?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 DAA(all topics are included in this link) : https://viden.io/knowledge/design-and-analysis-of-algorithms-topic-wise-ada?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Advanced DBMS : https://viden.io/knowledge/advanced-dbms?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 for QM method-https://viden.io/knowledge/quine-mccluskey-method-qm-method?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 K-MAPS : https://viden.io/knowledge/k-maps-karnaugh-map?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Basics of logic gates : https://viden.io/knowledge/basics-of-logic-gates-and-more?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=last-minute-tutorials-1 Website: https://lmtutorials.com/ Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ For any queries or suggestions, kindly mail at: [email protected]
Views: 49149 Last Minute Tutorials
Apriori Algorithm with solved example|Find frequent item set in hindi | DWM | ML | BDA
Sample Notes : https://drive.google.com/file/d/19xmuQO1cprKqqbIVKcd7_-hILxF9yfx6/view?usp=sharing for notes fill the form : https://goo.gl/forms/C7EcSPmfOGleVOOA3 For full course:https://goo.gl/bYbuZ2 More videos coming soon so Subscribe karke rakho  :  https://goo.gl/85HQGm for full notes   please fill the form for notes :https://goo.gl/forms/MJD1mAOaTzyag64P2 For full hand made  notes of data warehouse and data mining  its only 200 rs once we get payment notification we will mail you the notes on your email id contact us at :[email protected] For full course :https://goo.gl/Y1UcLd Topic wise: Introduction to Datawarehouse:https://goo.gl/7BnSFo Meta data in 5 mins :https://goo.gl/7aectS Datamart in datawarehouse :https://goo.gl/rzE7SJ Architecture of datawarehouse:https://goo.gl/DngTu7 how to draw star schema slowflake schema and fact constelation:https://goo.gl/94HsDT what is Olap operation :https://goo.gl/RYQEuN OLAP vs OLTP:https://goo.gl/hYL2kd decision tree with solved example:https://goo.gl/nNTFJ3 K mean clustering algorithm:https://goo.gl/9gGGu5 Introduction to data mining and architecture:https://goo.gl/8dUADv Naive bayes classifier:https://goo.gl/jVUNyc Apriori Algorithm:https://goo.gl/eY6Kbx Agglomerative clustering algorithmn:https://goo.gl/8ktMss KDD in data mining :https://goo.gl/K2vvuJ ETL process:https://goo.gl/bKnac9 FP TREE Algorithm:https://goo.gl/W24ZRF Decision tree:https://goo.gl/o3xHgo more videos coming soon so channel ko subscribe karke rakho
Views: 119713 Last moment tuitions
What is BUSINESS RULE MINING? What does BUSINESS RULE MINING mean? BUSINESS RULE MINING meaning - BUSINESS RULE MINING definition - BUSINESS RULE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Business rule mining is the process of extracting essential intellectual business logic in the form of Business Rules from packaged or Legacy software applications, recasting them in natural or formal language, and storing them in a source rule repository for further analysis or forward engineering. The goal is to capture these legacy business rules in a way that the business can validate, control and change them over time. Business rule mining supports a Business rules approach, which is defined as a formal way of managing and automating an organization's business rules so that the business behaves and evolves as its leaders intend. It is also commonly conducted as part of an application modernization project evolving legacy software applications to service oriented architecture (SOA) solutions, transitioning to packaged software, redeveloping new in-house applications, or to facilitate knowledge retention and communication between business and IT professionals in a maintenance environment. Alternative approaches to rule mining are manual and automated. A manual approach involves the hand-writing of rules on the basis of subject matter expert interviews and the inspection of source code, job flows, data structures and observed behavior. Manually extracting rules is complicated by the difficulty of locating and understanding highly interdependent logic that has been interwoven into millions of lines of software code. An automated approach utilizes repository-based software to locate logical connections inherent within applications and extract them into a predetermined business rules format. With automation, an effective approach is to apply semantic structures to existing applications. By overlaying business contexts onto legacy applications, rules miners can focus effort on discovering rules from systems that are valuable to the business. Effort is redirected away from mining commoditized or irrelevant applications. Further, best practices coupled with various tool-assisted techniques of capturing programs’ semantics speeds the transformation of technical rules to true business rules. Adding business semantics to the analysis process allows users to abstract technical concepts and descriptors that are normal in an application to a business level that is consumable by a rules analyst. System integrators, software vendors, rules mining practitioners, and in-house development teams have developed technologies, proprietary methodologies and industry-specific templates for application modernization and business rule mining.
Views: 61 The Audiopedia
Data Mining with Weka (1.4: Building a classifier)
Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Building a classifier http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 72967 WekaMOOC
Tutorial 9 de SqlServer - Create RULE
Si te gusto el vídeo dale Like, compártelo y Suscribete :D Siguenos en: facebook: https://www.facebook.com/learnwtutorials twitter: https://twitter.com/LearnWtutorials learnwtutorials aprende programacion tutorial videos lenguaje de programacion computadora tecnologia
Views: 1676 LearnWtutorials
Data Mining - Clustering
What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
UML Class Diagram Tutorial
Learn how to make classes, attributes, and methods in this UML Class Diagram tutorial. There's also in-depth training and examples on inheritance, aggregation, and composition relationships. UML (or Unified Modeling Language) is a software engineering language that was developed to create a standard way of visualizing the design of a system. And UML Class Diagrams describe the structure of a system by showing the system’s classes and how they relate to one another. This tutorial explains several characteristics of class diagrams. Within a class, there are attributes, methods, visibility, and data types. All of these components help identify a class and explain what it does. There are also several different types of relationships that exist within UML Class Diagrams. Inheritance is when a child class (or subclass) takes on all the attributes and methods of the parent class (or superclass). Association is a very basic relationship where there's no dependency. Aggregation is a relationship where the part can exist outside the whole. And finally, Composition is when a part cannot exist outside the whole. A class would be destroyed if the class it's related to is destroyed. Further UML Class Diagram information: https://www.lucidchart.com/pages/uml/class-diagram —— Learn more and sign up: http://www.lucidchart.com Follow us: Facebook: https://www.facebook.com/lucidchart Twitter: https://twitter.com/lucidchart Instagram: https://www.instagram.com/lucidchart LinkedIn: https://www.linkedin.com/company/lucidsoftware —— Credits for Photos with Attribution Requirements Tortoise - by Niccie King - http://bit.ly/2uHaL1G Otter - by Michael Malz - http://bit.ly/2vrVoYt Slow Loris - by David Haring - http://bit.ly/2uiBWxg Creep - by Poorna Kedar - http://bit.ly/2twR4K8 Visitor Center - by McGheiver - http://bit.ly/2uip0Hq Lobby - by cursedthing - http://bit.ly/2twBWw9
Views: 453663 Lucidchart
Lecture - 34 Data Mining and Knowledge Discovery
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
A global constraint for closed frequent patterns
A global constraint for closed frequent patterns presented in the conference CP 2016 - Toulouse - France
Views: 273 Mehdi Maamar
Efficient Data Mining Model to Predict the Risk of Heart Disease for Diabetic Patient through Freque
Efficient Data Mining Model to Predict the Risk of Heart Disease for Diabetic Patient through Freque
Views: 38 1 Crore Projects
Frequent Pattern Mining
Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof Sandhya Kode
Views: 3967 Vidya-mitra
Association rule learning
Please Subscribe our goal is 5000 subscriber for this year :) In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Source:http://en.wikipedia.org/wiki/Association_rule_learning
Views: 493 Wikivoicemedia
WACV18: Efficient Multi-Attribute Similarity Learning Towards Attribute-based Fashion Search
Kenan Ak, Joo Hwee Lim, Jo Yew THAM, Ashraf A. Kassim In this paper, we propose an attribute-based query & retrieval system designed for fashion products. Our system addresses the problem of carrying out fashion searches by the query image and attribute manipulation, e.g. replacing long sleeve attribute of a dress to sleeveless. We present the attributes in two groups: (1) general attributes (category, gender etc.) and (2) special attributes (sleeve length, collar etc.). The special attributes are more suitable for the attribute manipulation and thus conducting searches. In order to solve the mentioned fashion search problem, it is crucial for the deep neural networks to understand attribute similarities. To facilitate more specific similarity learning, clothing items are represented by their structural sub-components or ”parts”. The parts are estimated using an unsupervised segmentation method and used inside the proposed Convolutional Neural Network (CNN) as an attention mechanism. Meaning, different parts are connected with the special attributes, e.g. sleeve part is connected with sleeve length attribute. With this mechanism, part-based triplet ranking constraint is applied to learn similarity of each special attribute independently from one another in a single network. In the end, the well-defined features are used to conduct the fashion search. Additionally, an adaptive relevance feedback module is used to personalize the fashion search process with the feature descriptions. For our experiments, a new dataset is constructed containing 101,021 images which consist of pure clothing items. Besides achieving decent retrieval results in our new dataset, the experiments show that proposed technique outperforms different baselines and is able to adapt towards user's requests.
DBSCAN - Density Based Clustering Method - Full technique with visual examples
Here we discuss DBSCAN which is one of the method that uses Density based clustering method. Here we discuss the Algorithm, shows some examples and also give advantages and disadvantages of DBSCAN. The url of dbscan in python : http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
Lecture 6: Dependency Parsing
Lecture 6 covers dependency parsing which is the task of analyzing the syntactic dependency structure of a given input sentence S. The output of a dependency parser is a dependency tree where the words of the input sentence are connected by typed dependency relations. Key phrases: Dependency Parsing. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Machine Learning #73 BIRCH Algorithm | Clustering
Machine Learning #73 BIRCH Algorithm | Clustering In this lecture of machine learning we are going to see BIRCH algorithm for clustering with example. BIRCH algorithm (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm which is used to perform hierarchical clustering over particularly large data-sets.The advantage of using BIRCH algorithm is its ability to incrementally & dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). single scan of the database is needed by BIRCH algorithm in most of the cases. Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 5199 Xoviabcs
Ashutosh Jadhav: Knowledge-driven Search Intent Mining
http://www.knoesis.org/aboutus/thesis_defense#jadhav ABSTRACT: Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases. While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from more than 100 million search queries from smarts devices (smartphones or tablets) and personal computers (desktops or laptops). SLIDES: http://www.slideshare.net/knoesis/ashutosh-thesis
Views: 179 Knoesis Center
Top -22 Figures of Speech in English (Part-1)
This video lesson illustrates the common Figures of Speech in English, with definitions and examples from various spheres of real life as well as literature. Do watch part-2 of this lesson : https://www.youtube.com/watch?v=K82A7QXBf-4 Also popular among students are the following lessons on 200 Most Important Idioms & phrases in English (useful for Competitive Exams) Lesson-1 (50 Idioms): https://youtu.be/U2D5pDGnmFA Lesson-2 (50 Idioms): https://youtu.be/e7_qZgBpQyQ About this lesson- The following Figures of Speech are covered in Part-1: 1. Simile 2. Metaphor 3. Personification 4. Apostrophe 5. Metonymy 6. Synecdoche 7. Onomatopoeia 8. Alliteration 9. Assonance 10. Pun Part-2 covers the following Figures of Speech: Antithesis Chiasmus Paradox Irony Rhetorical Question Hyperbole Understatement Litotes Anaphora Epistrophe Climax Anti-climax
Views: 722305 Vocabulary TV
Lecture 05 - Training Versus Testing
Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 5 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 17, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 141088 caltech
Data Warehousing & 3 Tier Architecture Of Data Warehouse | E-Technologies | UGC NET CS Preparations
In this video, we have discussed Data Warehousing & Three Tier Architecture Of Data Warehousing. SUBSCRIBE US For More Updates On UGC NET Exam.
A Fast Clustering Based Feature Subset Selection Algorithm for High Dimensional Data
Contact:-8121953811,8522991105,0404-65511811 Mail Id:[email protected] Website:- http://www.cloudstechnologies.in
Views: 373 Cloud Technologies
Foundations of Causal Discovery
Author: Frederick Eberhardt, Division of the Humanities and Social Sciences Abstract: The now widely used theory of causal graphical models considers causal relations among a set of statistical variables. The causal relations are represented in terms of a directed graph among the set of variables, and the task of causal discovery is to identify this causal structure on the basis of the probability distribution generated by the variables in the graph. I will provide an introduction and overview of some of the methods for causal discovery and present known identifiability results with a particular focus on the assumptions they depend on. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 279 KDD2016 video
chapter 3 part II: Data Description
Coefficient of Variation, The Empirical (Normal) Rule, Measures of Position, Exploratory Data Analysis, The Five-Number Summary and Boxplots
Views: 2048 mora mrmor
INCREMENTAL K MEANS | Summer Project | Sreeparna Deb
This is just the brief explanation of the incremental k means algorithm which I derived as a part of my project. Due to time constraints I couldn't explain it elaborately. Anyone who is interested to know the detailed algorithm drop a mail and I will forward the detailed explanation to you. THANK YOU! [email protected]
Views: 506 Sreeparna Deb
Lecture 12 - Regularization
Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay. Lecture 12 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 10, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 81574 caltech
Enabling Integrated Search and Exploration Over Large Multidimensional Data
The need for rich, ad-hoc data analysis is key for pervasive discovery. However, generic and reusable systems tools for interactive search, exploration and mining over large data sets are lacking. Exploring large data sets interactively requires advanced data-driven search techniques that go well beyond the conventional database querying capabilities, whereas state-of-the-art search technologies are not designed and optimized to work for large out-of-core data sets. These requirements force users to roll their own custom solutions, typically by gluing together existing libraries, databases and custom scripts, only to end up with a solution that is difficult to develop, scale, optimize, maintain and reuse. To address these limitations, we propose a tight integration of data management and search technologies. This combination would not only allow users to perform search efficiently, but also offer a single, expressive framework that can support a wide variety of data-intensive search and exploration tasks. As the first step in this direction, we describe a custom search framework called Semantic Windows, which allows users to conveniently perform structured search via shape and content constraints over a multidimensional data space. As the second step, we describe a general-purpose exploration framework called Searchlight, which allows Constraint Programming (CP) machinery to run efficiently inside a Database Management System (DBMS) without the need to extract, transform and move the data. This marriage concurrently offers the rich expressiveness and efficiency of constraint-based search and optimization provided by modern CP solvers, and the ability of DBMSs to store and query data at scale, resulting in an enriched functionality that can effectively support both data- and search-intensive applications. As such, Searchlight is the first system to support generic search, exploration and mining over large multidimensional data collections, going beyond point algorithms designed for point search and mining tasks.
Views: 44 Microsoft Research
Bayesian Network Based Classification
To use the data set to build a bayesian network based classification model.
How to calculate linear regression using least square method
An example of how to calculate linear regression line using least squares. A step by step tutorial showing how to develop a linear regression equation. Use of colors and animations. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos Playlist on Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C SPSS Using Regression http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL Like us on: http://www.facebook.com/PartyMoreStudyLess David Longstreet Professor of the Universe Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 634164 statisticsfun
Detecting News from Microblogs  using Sequential Pattern Mining - Andy Lau QUT
Large amounts of news related information are available on microblog platforms such as Twitter, but this information is scattered and the volume of tweets makes it tedious and difficult for human to filter irrelevant information and extract meaningful topics. This thesis presents a microblog news detection framework using sequential pattern model. We design Pattern Model for Microblog (PMM) to represent topics as ordered list of terms. PMM effectively capture key topics in news such as persons, locations, organizations and events. Topic importance is then measured by evaluating topic weights using pattern properties and Twitter characteristics.
Lecture - 30 Introduction to Data Warehousing and OLAP
Lecture Series on Database Management System by Dr.S.Srinath, IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 209434 nptelhrd
Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies." Lecture Description Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery. View slides from this lecture: https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E About the Speaker Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
text mining, web mining and sentiment analysis
text mining, web mining
Views: 1397 Kakoli Bandyopadhyay
Binning Pattern for Data Smoothing | Partitioning Patterns | MapReduce Design Patterns | Edureka
Watch Sample Class recording: http://www.edureka.co/mapreduce-design-patterns?utm_source=youtube&utm_medium=referral&utm_campaign=mapreduce-binning-tut MapReduce Design Pattern is a template for solving a common and general data manipulation problem with MapReduce. A pattern is not specific to a domain such as text processing or graph analysis, but it is a general approach to solving a problem.Using design patterns is all about using tried and true design principles to build better software. Video gives a brief insight of following topics: 1.Understand Binning Pattern 2.Intro to Partitioning Pattern 3.Understand constraints and design paradigm Mapreduce 4.Understand Summarization Patterns Related Blog : http://www.edureka.co/blog/tailored-big-data-solutions-using-mapreduce-design-patterns?utm_source=youtube&utm_medium=webinar&utm_campaign=mapreduce-binning-tut 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 Summarization Design Patterns have extensively been covered in our course 'MapReduce Design Patterns’. 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: 2551 edureka!
Facebook CEO Mark Zuckerberg testifies before Congress on data scandal
Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 125739 CBC News
Lecture 10 - Neural Networks
Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 339476 caltech
2016 Lecture 06 Maps of Meaning: Part I: The primordial narrative
Our experience takes narrative form, under the influence of biological, cultural and uniquely individual forces. This is partly because our minds are based in social cognition. Experience manifests itself comprehensibly as the unknown itself, the great dragon of chaos; the unknown as we experience it, the great mother; culture, the great father, and the individual, the center of conscious being, Each of these categories manifests itself in action. Each has a positive and negative element. Want to support this channel? Patreon: https://www.patreon.com/jordanbpeterson Self Authoring: http://selfauthoring.com/ Jordan Peterson Website: http://jordanbpeterson.com/ Podcast: http://jordanbpeterson.com/jordan-b-peterson-podcast/ Reading List: http://jordanbpeterson.com/2017/03/great-books/ Twitter: https://twitter.com/jordanbpeterson
Views: 87984 Jordan B Peterson
Views: 9356 VLSI Physical Design
Evaluation Metrics for Clustering and Filtering Tasks
Talk #3: Dr. Julio Gonzalo, Universidad Nacional de Educacion a Distancia Day 1: Mon 31 Aug 2015, afternoon
Views: 96 essir2015
Clustering method in data mining is used to find the similarities in data and form them into subgroups for further analysis. All clustering methods have to assume that there exists some cluster relationship among the data objects which is to be applied on. The similarity between a pair of objects can be defined either explicitly or implicitly. The major difference between a traditional dissimilarity/similarity measure and our proposed method is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints in which the objects to be measured are assumed in different cluster. Using multiple viewpoints, more informative assessment of similarity could be achieved.
SimSched: How to calculate the economic values
On this video I'm going to explain step by step how to calculate the blocks economic values, and I'll give a explanation about how these parameters can strongly affect your mining project. Every block must have an economic value for each possible destination, which are the processing plants and waste dumps. This field will report the value of each block as a function of its destination, grade, recovery, mining cost, transportation, processing, selling costs and prices, among others. Then, SimSched will decide, based on these values, whether and where each block should be processed, discarded or stockpiled. Choosing the one that contributes to maximize the NPV of the project, given all the inter-related constraints from the scheduling optimization, in the case of SimSched Direct Block Scheduler. Please access our website at http://www.miningmath.com/software.html Send your questions to our support service e-mail [email protected]
Views: 349 Rodolfo Ota