'Same cluster' diaporamas de présentation

2011 STAR Interpreting and Using Results

2011 STAR Interpreting and Using Results. August 10, 2011 Webcast Webcast starts at 9:00 a.m. Objectives. Workshop participants will be able to: Describe the purposes of STAR reports Interpret STAR results Explain key statistics Compare and contrast types of reports

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Close Relationships and Interpersonal Attraction

1.71k views • 61 slides

An Introduction to Data Mining

An Introduction to Data Mining. Prof. S. Sudarshan CSE Dept, IIT Bombay Most slides courtesy: Prof. Sunita Sarawagi School of IT, IIT Bombay. Why Data Mining. Credit ratings/targeted marketing :

615 views • 47 slides

An Introduction to Data Mining

An Introduction to Data Mining. Prof. S. Sudarshan CSE Dept, IIT Bombay Most slides courtesy: Prof. Sunita Sarawagi School of IT, IIT Bombay. Why Data Mining. Credit ratings/targeted marketing :

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Linking Records with Value Diversity

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Annotating Topics of Opinions

Annotating Topics of Opinions. Veselin Stoyanov Claire Cardie. Talk Overview. Fine-grained sentiment analysis Definitions Examples Opinion topic annotation Definitions Issues Approach and Corpus IA agreement. Background. Sentiment Analysis:

785 views • 42 slides

HCS Clustering Algorithm

HCS Clustering Algorithm. A Clustering Algorithm Based on Graph Connectivity. Presentation Outline. The Problem HCS Algorithm Overview Main Players General Algorithm Properties Improvements Conclusion. The Problem. Clustering:

633 views • 42 slides

Clustering

Clustering. Idea and Applications. Clustering is the process of grouping a set of physical or abstract objects into classes of similar objects. It is also called unsupervised learning. It is a common and important task that finds many applications. Applications in Search engines:

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Clustering III

Clustering III. Lecture outline. Soft (model-based) clustering and EM algorithm Clustering aggregation [A. Gionis, H. Mannila, P. Tsaparas: Clustering aggregation, ICDE 2004] Impossibility theorem for clustering [Jon Kleinberg, An impossibility theorem for clustering, NIPS 2002].

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Scalable K-Means++

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Overview of the TDT 2001 Evaluation and Results

Overview of the TDT 2001 Evaluation and Results. Jonathan Fiscus Gaithersburg Holiday Inn Gaithersburg, Maryland November 12-13, 2001. Outline. TDT Evaluation Overview 2001 TDT Evaluation Result Summaries First Story Detection (FSD) Topic Detection Topic Tracking Link Detection

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Cluster Analysis

Cluster Analysis. Cluster Analysis. What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Model-Based Clustering Methods. Model based clustering.

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Classification: Cluster Analysis and Related Techniques

Classification: Cluster Analysis and Related Techniques. Tanya , Caroline , Nick. Introduction to Classification. Search for divisions within data ? identify groups of individuals with similar characteristics and cluster them together

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Lecture 7 Outline

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Classification: Cluster Analysis and Related Techniques

Classification: Cluster Analysis and Related Techniques. Tanya , Caroline , Nick. Introduction to Classification. Search for divisions within data ? identify groups of individuals with similar characteristics and cluster them together

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Topic9: Density-based Clustering

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http://variability.org

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Data Movement Mechanisms Malcolm Atkinson Director of National e-Science Centre www.nesc.ac.uk 1 st May 2003 IBM Almade

Data Movement Mechanisms Malcolm Atkinson Director of National e-Science Centre www.nesc.ac.uk 1 st May 2003 IBM Almaden Research Centre DAIS WG Face-to-Face. Motivation. Results of no value If not delivered to a consumer Updates Need supplied values These may be large!

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