Categorical data visualization and clustering using subjective factors

Chia Hui Chang; Zhi Kai Ding

doi:10.1007/978-3-540-30076-2_23

Categorical data visualization and clustering using subjective factors

Chia Hui Chang, Zhi Kai Ding

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Scopus citations

Abstract

A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Yahiko Kambayashi, Mukesh Mohania, Wolfram Wöß
Publisher	Springer Verlag
Pages	229-238
Number of pages	10
ISBN (Print)	354022937X, 9783540229377
DOIs	https://doi.org/10.1007/978-3-540-30076-2_23
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3181
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-30076-2_23

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Chang, C. H., & Ding, Z. K. (2004). Categorical data visualization and clustering using subjective factors. In Y. Kambayashi, M. Mohania, & W. Wöß (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 229-238). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3181). Springer Verlag. https://doi.org/10.1007/978-3-540-30076-2_23

Chang, Chia Hui ; Ding, Zhi Kai. / Categorical data visualization and clustering using subjective factors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Yahiko Kambayashi ; Mukesh Mohania ; Wolfram Wöß. Springer Verlag, 2004. pp. 229-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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author = "Chang, {Chia Hui} and Ding, {Zhi Kai}",

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Chang, CH & Ding, ZK 2004, Categorical data visualization and clustering using subjective factors. in Y Kambayashi, M Mohania & W Wöß (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3181, Springer Verlag, pp. 229-238. https://doi.org/10.1007/978-3-540-30076-2_23

Categorical data visualization and clustering using subjective factors. / Chang, Chia Hui; Ding, Zhi Kai.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Yahiko Kambayashi; Mukesh Mohania; Wolfram Wöß. Springer Verlag, 2004. p. 229-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3181).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - Categorical data visualization and clustering using subjective factors

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N1 - Funding Information: This paper was sponsored by National Science Council, Taiwan under grant NSC92-2524-S-008-002.

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N2 - A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Kambayashi, Yahiko

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PB - Springer Verlag

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Chang CH, Ding ZK. Categorical data visualization and clustering using subjective factors. In Kambayashi Y, Mohania M, Wöß W, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 229-238. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-30076-2_23

Categorical data visualization and clustering using subjective factors

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