A DNA-based clustering method based on statistics adapted to heterogeneous coordinate data

Ikno Kim, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    A cluster analysis is often used in social sciences, management, general science and engineering, etc. with the objective of characterising structures in heterogeneous data sets. In this case, collections of information granules are obviously constructed through clustering techniques. However, clustering problems are intractable and NP-complete problems with a number of patterns. In this article, we discuss the use of DNA computing as a vehicle of heterogeneous coordinated data clustering, and elaborate on the fundamentals of DNA computing in the context of clustering tasks. A novel DNA-based clustering method is proposed, using statistics-based encoding of DNA strands, for clustering coordinated data from simulated DNA studies and experiments. The results also show the capabilities of this method when adapted to heterogeneous coordinate data.

    Original languageEnglish
    Title of host publicationProceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009
    Pages892-897
    Number of pages6
    DOIs
    Publication statusPublished - 2009
    EventInternational Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009 - Fukuoka
    Duration: 2009 Mar 162009 Mar 19

    Other

    OtherInternational Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009
    CityFukuoka
    Period09/3/1609/3/19

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Software
    • Control and Systems Engineering

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  • Cite this

    Kim, I., & Watada, J. (2009). A DNA-based clustering method based on statistics adapted to heterogeneous coordinate data. In Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009 (pp. 892-897). [5066896] https://doi.org/10.1109/CISIS.2009.35