data clustering: 50 years beyond k-means

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Data Clustering: 50 years beyond K-means

Presenter : Jiang-Shan Wang

Authors : Anil K. Jain

PRL 2010

國立雲林科技大學National Yunlin University of Science and Technology

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I. M.Outline

Motivation

Objective

Data clustering

User’s dilemma

K-means

Extensions of K-means

Trends in data clustering

Summary

Comments

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N.Y.U.S.T.

I. M.Motivation

Providing a brief overview of clustering and point out some of the emerging and useful research directions.

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I. M.Objective

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Summarizing well known clustering methods, discuss the major challenge and key issues in designing clustering algorithm, and point out some of the emerging and useful research directions.


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I. M.Data clustering

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Three main purposes: Underlying structure

Natural classification

Compression


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I. M.K-means

Three parameters Number of clusters

Cluster initialization

Distance metrics

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I. M.Extensions of K-means

Fuzzy C-means

Bisecting K-means

X-means

K-medoid

Kernel K-means

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N.Y.U.S.T.

I. M.User’s dilemma

Representation

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Purpose of grouping

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Number of clusters

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Cluster validity

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Comparing clustering algorithm

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Comparing clustering algorithm

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Admissibility analysis of clustering algorithms

Fisher and vanNess’s criteria Convex

Cluster proportion

Cluster omission

Monotone

Kleinberg’s criteria Scale invariance

Richness

consistency

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I. M.Trends in data clustering

Clustering ensembles

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Semi-supervised clustering

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Large-scale clustering

Studies Efficient Nearest Neighbor

Data summarization

Distributed computing

Incremental clustering

Sampling-based methods

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Multi-way clustering

Heterogeneous data Rank data

Dynamic data

Graph data

Relational data

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I. M.Summary

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There needs to be a suite of benchmark data.

A tighter integration between clustering algorithms and the application needs.

Optimization problems.

Stability or consistency.

Choose clustering principles according to satisfiability of the stated axioms.

Develop semi-supervised clustering.


N.Y.U.S.T.

I. M.Comments

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Advantage Many figures to understanding.

Drawback …

Application Clustering.

data clustering: 50 years beyond k-means

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