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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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Presentation Transcript
outline
Outline
  • Motivation
  • Objective
  • Research restrict
  • Literature review
    • An overview of related clustering algorithms
    • The limitations of clustering algorithms
  • CHAMELEON
  • Concluding remarks
  • Personal opinion
motivation
Motivation
  • Existing clustering algorithms can breakdown
    • Choice of parameters is incorrect
    • Model is not adequate to capture the characteristics of clusters
    • Diverse shapes, densities, and sizes
objective
Objective
  • Presenting a novel hierarchical clustering algorithm – CHAMELEON
    • Facilitating discovery of natural and homogeneous
    • Being applicable to all types of data
research restrict
Research Restrict
  • In this paper, authors ignored the issue of scaling to large data sets that cannot fit in the main memory
literature review
Literature Review
  • Clustering
  • An overview of related clustering algorithms
  • The limitations of the recently proposed state of the art clustering algorithms
clustering
Clustering
  • The intracluster similarity is maximized and the intercluster similarity is minimized [Jain and Dubes, 1988]
  • Serving as the foundation for data mining and analysis techniques
clustering cont d
Clustering(cont’d)
  • Applications
    • Purchasing patterns
    • Categorization of documents on WWW [Boley, et al., 1999]
    • Grouping of genes and proteins that have similar functionality[Harris, et al., 1992]
    • Grouping if spatial locations prone to earth quakes[Byers and Adrian, 1998]
an overview of related clustering algorithms
An Overview of Related Clustering Algorithms
  • Partitional techniques
  • Hierarchical techniques
partitional techniques
Partitional Techniques
  • K means[Jain and Dubes, 1988]
hierarchical techniques
Hierarchical Techniques
  • CURE [Guha, Rastogi and Shim, 1998]
  • ROCK [Guha, Rastogi and Shim, 1999]
limitations of existing hierarchical schemas
Limitations of Existing Hierarchical Schemas
  • CURE
    • Fail to take into account special characteristics
limitations of existing hierarchical schemas cont d
Limitations of Existing Hierarchical Schemas(cont’d)
  • ROCK
    • Irrespective of densities and shapes
chameleon
CHAMELEON
  • Overview
  • Modeling the data
  • Modeling the cluster similarity
  • A two-phase clustering algorithm
  • Performance analysis
  • Experimental Results
modeling the data
Modeling the Data
  • K-nearest graphs from an original data in 2D
modeling the cluster similarity
Modeling the Cluster Similarity
  • Relative inter-connectivity
a two phase clustering algorithm
A Two-phase Clustering Algorithm
  • Phase I: Finding initial sub-clusters
a two phase clustering algorithm cont d
A Two-phase Clustering Algorithm(cont’d)
  • Phase I: Finding initial sub-clusters
    • Multilevel paradigm[Karypis & Kumar, 1999]
    • hMeT|s [Karypis & Kumar, 1999]
a two phase clustering algorithm cont d21
A Two-phase Clustering Algorithm(cont’d)
  • Phase II: Merging sub-clusters using a dynamic framework

TRI, TRC: user specified threshold

a two phase clustering algorithm cont d22
A Two-phase Clustering Algorithm(cont’d)
  • Phase II: Merging sub-clusters using a dynamic framework
performance analysis
Performance Analysis
  • The amount of time required to compute
    • K-nearest neighbor graph
    • Two-phase clustering
performance analysis cont d
Performance Analysis(cont’d)
  • The amount of time required to compute
    • K-nearest neighbor graph
      • Low-dimensional data sets = O(n log n)
      • High-dimensional data sets = O(n2)
performance analysis cont d25
Performance Analysis(cont’d)
  • The amount of time required to compute
    • Two-phase clustering
      • Computing internal inter-connectivity and closeness for each cluster: O(nm)
      • Selecting the most similar pair of cluster: O(n log n + m2 log m)
    • Total time = O(nm + n log n + m2 log m)
experimental results
Experimental Results
  • Program
    • DBSCAN: a publicly available version
    • CURE: a locally implemented version
  • Data sets
  • Qualitative comparison
data sets
Data Sets
  • Five clusters
  • Different size, shape, and density
  • Noise point
  • Two clusters
  • Close to each other
  • Different region, different densities
  • Six clusters
  • Different size, shape, and orientation
  • Random noise point
  • Special artifacts
  • Eight clusters
  • Different size, shape, density, and orientation
  • Random noise point
  • Eight clusters
  • Different size, shape, and orientation
  • Random noise and special artifacts
concluding remarks
Concluding remarks
  • CHAMELEON can discover natural clusters of different shapes and sizes
  • It is possible to use other algorithms instead of k-nearest neighbor graph
  • Different domains may require different models for capturing closeness and inter-connectivity
personal opinion
Personal Opinion
  • Without further work