Research Article
CATCLUS – A Proposed Algorithm for Clustering Categorical Data
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 139 - Issue 10 |
| Published: April 2016 |
| Authors: Srikanta Kolay, Kumar S. Ray |
10.5120/ijca2016909394
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Srikanta Kolay, Kumar S. Ray . CATCLUS – A Proposed Algorithm for Clustering Categorical Data. International Journal of Computer Applications. 139, 10 (April 2016), 40-44. DOI=10.5120/ijca2016909394
@article{ 10.5120/ijca2016909394,
author = { Srikanta Kolay,Kumar S. Ray },
title = { CATCLUS – A Proposed Algorithm for Clustering Categorical Data },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 139 },
number = { 10 },
pages = { 40-44 },
doi = { 10.5120/ijca2016909394 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A Srikanta Kolay
%A Kumar S. Ray
%T CATCLUS – A Proposed Algorithm for Clustering Categorical Data%T
%J International Journal of Computer Applications
%V 139
%N 10
%P 40-44
%R 10.5120/ijca2016909394
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Classification of categorical data always involves more complexities compared to the numerical data. Because, a firm outline cannot be drawn in case of categorical data. Different types of assumptions are followed by various researchers to treat such kind of data. Again, dissimilarity measures applied in case of numerical data cannot be applied directly in this case. In this paper, a new clustering algorithm for categorical data is proposed. The algorithm is using a newly devised dissimilarity measure. This paper only includes the theoretical description of the proposed algorithm with appropriate example.
References
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- S. Guha, R. Rastogi, and K. Shim,” ROCK: A Robust Clustering Algorithm for Categorical Attributes”, 15th International Conference on Data Engineering, pp. 512-521, 2000.
- V., Ganti, J. Gehrke, R. Ramakrishnan, CACTUS – clustering categorical data using summaries, in: Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 73–83.
- Z. He, X. Xu, S. Deng, Squeezer: an efficient algorithm for clustering categorical data Journal of Computer Science & Technology, 17 (5) (2002), pp. 611–624
- D. Kim, K. Lee, D. Lee Fuzzy clustering of categorical data using fuzzy centroids Pattern Recognition Letters, 25 (11) (2004), pp. 1263–1271
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