Intelligent Database Systems Lab N.Y.U.S.T. I. M. Validity index for clusters of different sizes and densities Presenter: Jun-Yi Wu Authors: Krista Rizman

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.

Validity index for clusters of different sizes and densities

Presenter: Jun-Yi Wu　Authors: Krista Rizman Zalik, Borut Zalik

2011 PRL

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


N.Y.U.S.T.

I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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N.Y.U.S.T.

I. M.Motivation

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Most of the previous validity indices have been considerably dependent on the number of data objects in clusters, on cluster centroids and on average values.

Most popular validity measures have the tendency to ignore clusters with low density and are not efficient in validation of partitions having different sizes and densities.


N.Y.U.S.T.

I. M.Objective

Two cluster validity indices are proposed for efficient validation of partitions containing clusters that widely differ in sizes and densities.

To design a cluster validity index that is suitable for the validation of partitions having different sizes and densities.

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Overlap Compactness Separation distance

A good partitions:


N.Y.U.S.T.

I. M.Methodology

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Review several popular validity indicesDunn index; D Indx XiE index

Davies-Bouldin’s index; DB index

C index

G index

G+ index

Partition coefficient; PC index

Classification entropy; CE index


N.Y.U.S.T.

I. M.Methodology

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Review several popular validity indices.

D Index

DB Index

G+ Index

C Index

G Index

PC

CE

XiE


N.Y.U.S.T.

I. M.Methodology

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new clustering validity indices. SV-index

Validation of index SV

Fuzzification of the SV index

The proposed index OS exploiting overlap and separation measures

Overlap measure

Separation measure and validity index SV

Validation of index OS


N.Y.U.S.T.

I. M.Methodology

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SV-index

a measure for partition validity that consists of clusters that widely differ in density or size


N.Y.U.S.T.

I. M.Methodology

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Validation of index SV


N.Y.U.S.T.

I. M.Methodology

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Fuzzification of the SV index

A fuzzy version of the index SV is obtained by integrating the membership values in the variation measure.


N.Y.U.S.T.

I. M.Methodology

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The proposed index OS exploiting overlap and separation measure Experiment results suggested that inter-cluster separation plays a more

important role in cluster validation.

Indices are limited in their ability to compute the compactness and the separation in partitions having overlapping clusters and clusters of different sizes, which leads to an incorrect validation results.

Considering these results a cluster validity index is suggested based on an overlap and separation measures.


N.Y.U.S.T.

I. M.Methodology

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Overlap measure


N.Y.U.S.T.

I. M.Methodology

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Separation measure and validity index SV


N.Y.U.S.T.

I. M.Methodology

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Validation of index OS


N.Y.U.S.T.

I. M.Experiments

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To demonstrate the effectiveness of the proposed SV and OS indices for determining the optional number of clusters. Artificial data set A1

Artificial data set A2

Artificial data set A3

Iris data set

Wine data set

Glass data set


N.Y.U.S.T.

I. M.Experiments-Artificial data set A1

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N.Y.U.S.T.

I. M.Experiments -Iris data set.

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N.Y.U.S.T.

I. M.Experiments-Wine data set

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N.Y.U.S.T.

I. M.Experiments-Wine data set

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N.Y.U.S.T.

I. M.Conclusion

The experimental results proved that the new indices outperform the other considered indices, especially when cluster widely differ in sizes or densities.

A good partition is expected to have low degree of overlap and a larger separation distance and compactness.

The maximum value of the ratio of the SV index and the minimum value of the OS index indicate the optimal partition.

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N.Y.U.S.T.

I. M.Comments

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Advantage

Drawback ….

Application Clustering

Validity index

Documents

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Validity index for clusters of different sizes and densities Presenter: Jun-Yi Wu Authors: Krista Rizman