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Validity index for clusters of different sizes and densities. Presenter: Jun-Yi Wu Authors: Krista Rizman Zalik , Borut Zalik. 國立雲林科技大學 National Yunlin University of Science and Technology. 2011 PRL. Outline. Motivation Objective Methodology Experiments Conclusion Comments. - PowerPoint PPT Presentation
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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
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Methodology Experiments Conclusion Comments
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Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
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:
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Review several popular validity indicesDunn index; D Indx XiE indexDavies-Bouldin’s index; DB indexC indexG indexG+ indexPartition coefficient; PC index
Classification entropy; CE index
Intelligent Database Systems Lab
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
Intelligent Database Systems Lab
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
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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SV-indexa measure for partition validity that consists of clusters that widely differ in density or size
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Validation of index SV
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Fuzzification of the SV indexA fuzzy version of the index SV is obtained by integrating the membership values in the variation measure.
Intelligent Database Systems Lab
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.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Overlap measure
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Separation measure and validity index SV
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Validation of index OS
Intelligent Database Systems Lab
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
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments-Artificial data set A1
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments-Artificial data set A2
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments-Artificial data set A3
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments-Artificial data set A3
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments -Iris data set.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments-Wine data set
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments-Wine data set
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Intelligent Database Systems Lab
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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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
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Advantage
Drawback ….
Application Clustering Validity index