A proximity approach to DNA based clustering analysis

Rohani Binti Abu Bakar*, Junzo Watada, Witold Pedrycz

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)


    Clustering deals with huge amounts of data and aims at the discovery of their structure which becomes expressed in terms of a collection of clusters - information granules capturing the underlying topology of the data. The objective of this paper is to propose an algorithm to support clustering realized in the form of bio-soft or DNA computing. This approach is of particular interest when dealing with large and heterogeneous data sets and when being faced with an unknown number of clusters. We present the details of the algorithm of proximity clustering and show how the overall computing is supported by the individual mechanisms of DNA processing. We offer a numerical example to illustrate essential aspects of the DNA-based clustering. ICIC International

    Original languageEnglish
    Pages (from-to)1203-1212
    Number of pages10
    JournalInternational Journal of Innovative Computing, Information and Control
    Issue number5
    Publication statusPublished - 2008 May


    • Cluster validity
    • Clustering
    • DNA computing
    • Optimization
    • Proximity

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Information Systems
    • Software
    • Theoretical Computer Science


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