Decision tree based clustering

Dongsuk Yook

doi:10.1007/3-540-45675-9_73

Decision tree based clustering

Dongsuk Yook

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Adecision tree can be used not only as a classifier but also as a clustering method. One of such applications can be found in automatic speech recognition using hidden Markov models (HMMs). Due to the insufficient amount of training data, similar states of triphone HMMs are grouped together using a decision tree to share a common probability distribution. At the same time, in order to predict the statistics of unseen triphones, the decision tree is used as a classifier as well. In this paper, we study several cluster split criteria in decision tree building algorithms for the case where the instances to be clustered are probability density functions. Especially, when Gaussian probability distributions are to be clustered, we have found that the Bhattacharyya distance based measures are more consistent than the conventional log likelihood based measure.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings
Editors	Hujun Yin, Nigel Allinson, Richard Freeman, John Keane, Simon Hubbard
Publisher	Springer Verlag
Pages	487-492
Number of pages	6
ISBN (Print)	9783540440253
DOIs	https://doi.org/10.1007/3-540-45675-9_73
Publication status	Published - 2002
Event	3rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2002 - Manchester, United Kingdom Duration: 2002 Aug 12 → 2002 Aug 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2412
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2002
Country/Territory	United Kingdom
City	Manchester
Period	02/8/12 → 02/8/14

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45675-9_73

Cite this

Yook, D. (2002). Decision tree based clustering. In H. Yin, N. Allinson, R. Freeman, J. Keane, & S. Hubbard (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings (pp. 487-492). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2412). Springer Verlag. https://doi.org/10.1007/3-540-45675-9_73

Decision tree based clustering. / Yook, Dongsuk.

Intelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings. ed. / Hujun Yin; Nigel Allinson; Richard Freeman; John Keane; Simon Hubbard. Springer Verlag, 2002. p. 487-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2412).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yook, D 2002, Decision tree based clustering. in H Yin, N Allinson, R Freeman, J Keane & S Hubbard (eds), Intelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2412, Springer Verlag, pp. 487-492, 3rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2002, Manchester, United Kingdom, 02/8/12. https://doi.org/10.1007/3-540-45675-9_73

Yook D. Decision tree based clustering. In Yin H, Allinson N, Freeman R, Keane J, Hubbard S, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings. Springer Verlag. 2002. p. 487-492. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45675-9_73

Yook, Dongsuk. / Decision tree based clustering. Intelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings. editor / Hujun Yin ; Nigel Allinson ; Richard Freeman ; John Keane ; Simon Hubbard. Springer Verlag, 2002. pp. 487-492 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Adecision tree can be used not only as a classifier but also as a clustering method. One of such applications can be found in automatic speech recognition using hidden Markov models (HMMs). Due to the insufficient amount of training data, similar states of triphone HMMs are grouped together using a decision tree to share a common probability distribution. At the same time, in order to predict the statistics of unseen triphones, the decision tree is used as a classifier as well. In this paper, we study several cluster split criteria in decision tree building algorithms for the case where the instances to be clustered are probability density functions. Especially, when Gaussian probability distributions are to be clustered, we have found that the Bhattacharyya distance based measures are more consistent than the conventional log likelihood based measure.",

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Decision tree based clustering

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