Robustness of DNA-based clustering

Rohani Abu Bakar*, Chu Yu-Yi, Junzo Watada

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter


    The primary objective of clustering is to discover a structure in the data by forming some number of clusters or groups. In order to achieve optimal clustering results in current soft computing approaches, two fundamental questions should be considered; (i) how many clusters should be actually presented in the given data, and (ii) how real or good the clustering itself is. Based on these two fundamental questions, almost clustering method needs to determine the number of clusters. Yet, it is difficult to determine an optimal number of a cluster group should be obtained for each data set. Hence, DNA-based clustering algorithms were proposed to solve clustering problem without considering any preliminary parameters such as a number of clusters, iteration and, etc. Because of the nature of processes between DNA-based solutions with a silicon-based solution, the evaluation of obtained results from DNA-based clustering is critical to be conducted. It is to ensure that the obtained results from this proposal can be accepted as well as other soft computing techniques. Thus, this study proposes two different techniques to evaluate the DNA-based clustering algorithms either it can be accepted as other soft computing techniques or the results that obtained from DNA-based clustering are not reliable for employed.

    Original languageEnglish
    Title of host publicationStudies in Computational Intelligence
    Number of pages18
    Publication statusPublished - 2011

    Publication series

    NameStudies in Computational Intelligence
    ISSN (Print)1860949X

    ASJC Scopus subject areas

    • Artificial Intelligence


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