Self-organising maps for tree view based hierarchical document clustering

Richard Freeman, Hujun Yin, Nigel M. Allinson

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    In this paper we investigate the use of Self-Organising Maps (SOM) for document clustering. Previous methods using the SOM to cluster documents have used two-dimensional maps. This paper presents a hierarchical and growing method using a series of one-dimensional maps instead. Using this type of SOM is an efficient method for clustering documents and browsing them in a dynamically generated tree of topics. These topics are automatically discovered for each cluster, based on the set of document in a particular cluster. We demonstrate the efficiency of the method using different sets of real world web documents.
    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
    PublisherIEEE
    Pages1906-1911
    Number of pages5
    Volume2
    Publication statusPublished - 2002
    Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI
    Duration: 1 Jul 2002 → …

    Conference

    Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
    CityHonolulu, HI
    Period1/07/02 → …

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