On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm

Author

Ng, Michael K. ; Li, Mark Junjie ; Huang, Joshua Zhexue ; He, Zengyou

Author_Institution

Dept. of Math., Hong Kong Baptist Univ., Kowloon

Volume

29

Issue

3

fYear

2007

fDate

3/1/2007 12:00:00 AM

Firstpage

503

Lastpage

507

Abstract

This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in (Z. He, et al., 2005), (O. San, et al., 2004) which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework

Keywords

data analysis; data mining; pattern clustering; categorical data; data clustering; data mining; k-modes clustering algorithm; matching dissimilarity measure; Algorithm design and analysis; Clustering algorithms; Convergence; Cost function; Database systems; Frequency measurement; Helium; Data mining; categorical data.; clustering; k-modes algorithm; Algorithms; Artifacts; Artificial Intelligence; Cluster Analysis; Information Storage and Retrieval; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2007.53

Filename

4069266