'Cluster similarity' presentation slideshows

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High Performance Data Mining Chapter 3: Clustering

High Performance Data Mining Chapter 3: Clustering. Vipin Kumar Army High Performance Computing Research Center Department of Computer Science University of Minnesota http://www.cs.umn.edu/~kumar. Chapter 3: Clustering Algorithms. Outline K-means Clustering Hierarchical Clustering

914 views • 64 slides

Lecture 17: Hierarchical Clustering

Lecture 17: Hierarchical Clustering. Hierarchical clustering. Our goal in hierarchical clustering is to create a hierarchy like the one we saw earlier in Reuters: We want to create this hierarchy automatically . We can do this either top-down or bottom-up . The best known

671 views • 44 slides

IP ComVICS , Brno, 28 June 2013 Nuno Escudeiro, nfe@isep.ipp.pt

IP ComVICS , Brno, 28 June 2013 Nuno Escudeiro, nfe@isep.ipp.pt. Outline Introduction to text mining Text modeling , bag -of- words , TFxIDF Text processing Tools for text mining R language , tm package Case study Hands-on contest. INTRODUCTION.

526 views • 39 slides

Data Mining Cluster Analysis: Advanced Concepts and Algorithms

786 views • 37 slides

CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling

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CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling

CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. Outline. Motivation Objective Research restrict Literature review An overview of related clustering algorithms The limitations of clustering algorithms CHAMELEON Concluding remarks Personal opinion. Motivation.

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Bursty Subgraphs in Social Network

416 views • 28 slides

Unusual Activity in Video

Unusual Activity in Video. Presented by wei pan. Blackjack. Video. Keywords for Video. Left to Middle: Middle to Right:. Derivative Filters in 1 Minute. Signal. *. Gt. Keywords for Video. Left to Middle: Middle to Right:. Histogram. Keywords for Video. Co- occurance Matrix. CP.

273 views • 15 slides

Data Reduction via Instance Selection

Data Reduction via Instance Selection. Chapter 1. Background. KDD Nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. Instance selection

244 views • 13 slides

Automatic Cluster Detection

453 views • 8 slides