Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes

Bidyut Kr. Patra, Ville Ollikainen, Raimo Launonen, Sukumar Nandi, Korra Sathya Babu

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

    9 Citations (Scopus)


    Clustering has been recognized as one of the important tasks in data mining. One important class of clustering is distance based method. To reduce the computational and storage burden of the classical clustering methods, many distance based hybrid clustering methods have been proposed. However, these methods are not suitable for cluster analysis in dynamic environment where underlying data distribution and subsequently clustering structures change over time. In this paper, we propose a distance based incremental clustering method, which can find arbitrary shaped clusters in fast changing dynamic scenarios. Our proposed method is based on recently proposed al-SL method, which can successfully be applied to large static datasets. In the incremental version of the al-SL (termed as IncrementalSL), we exploit important characteristics of al-SL method to handle frequent updates of patterns to the given dataset. The IncrementalSL method can produce exactly same clustering results as produced by the al-SL method. To show the effectiveness of the IncrementalSL in dynamically changing database, we experimented with one synthetic and one real world datasets
    Original languageEnglish
    Title of host publicationPattern Recognition and Machine Intelligence
    Subtitle of host publicationPReMI 2013
    ISBN (Electronic)978-3-642-45062-4
    ISBN (Print)978-3-642-45061-7
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    Event5th International Conference, PReMI 2013 - Kolkata, India
    Duration: 10 Dec 201314 Dec 2013

    Publication series

    SeriesLecture Notes in Computer Science


    Conference5th International Conference, PReMI 2013
    Abbreviated titlePReMI 2013


    • Incremental clustering
    • arbitrary shaped clusters
    • large datasets


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