Decision tree based clustering

    Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
    Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2002 - 3rd International Conference, Proceedings
    EditorsHujun Yin, Nigel Allinson, Richard Freeman, John Keane, Simon Hubbard
    PublisherSpringer Verlag
    Pages487-492
    Number of pages6
    ISBN (Print)9783540440253
    DOIs
    Publication statusPublished - 2002
    Event3rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2002 - Manchester, United Kingdom
    Duration: 2002 Aug 122002 Aug 14

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2412
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other3rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2002
    Country/TerritoryUnited Kingdom
    CityManchester
    Period02/8/1202/8/14

    Bibliographical note

    Publisher Copyright:
    © Springer-Verlag Berlin Heidelberg 2002.

    Copyright:
    Copyright 2020 Elsevier B.V., All rights reserved.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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