SELF-ORGANIZING MAP BASED CLUSTERING MODEL BY ANALYZING EIGEN SYSTEM OF PCA

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Published: Feb 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i1.5191
Keywords:
Cluster Analysis, Self Organizing Map, Principal Component Analysis.

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Parthajit Roy
The University of Burdwan
Swati Adhikari

Abstract

Two novel clustering techniques, based on Principal Component Analysis (PCA), have been proposed in this paper that use Self Organizing Map as clustering model. The proposed models are differed by the number of principal components selection techniques in PCA and are applicable on clustering of non-categorical data. The present paper proposes, either to cluster the eigenvalues or to cluster the eigenvectors of the covariance matrix of the associated dataset in order to determine the number of principal components to be selected in PCA. It is also proposed that it is possible to further improve the performance of the SOM based clustering model by using either of the proposed techniques to select number of principal components. The benchmark wine dataset is used for testing purpose. Two existing principal components selection methods are used to evaluate the proposed clustering models.

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Vol. 9 No. 1 (2018): January-February 2018
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Author Biography

Parthajit Roy, The University of Burdwan

Assistant Professor

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