A new topological clustering algorithm for interval data

Guénaël Cabanes, Younès Bennani, Renaud Destenay, André Hardy

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    Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled data sets. In this paper we focus on interval data: i.e., where the objects are defined as hyper-rectangles. We propose here a new clustering algorithm for interval data, based on the learning of a Self-Organizing Map. The major advantage of our approach is that the number of clusters to find is determined automatically; no a priori hypothesis for the number of clusters is required. Experimental results confirm the effectiveness of the proposed algorithm when applied to interval data.

    Original languageEnglish
    Pages (from-to)3030-3039
    Number of pages10
    JournalPattern Recognition
    Issue number11
    Publication statusPublished - 1 Nov 2013


    • Clustering
    • Interval data
    • Self-organizing map

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