Abstract
Invention of microarray DNA technology has paved the way of clustering gene expression data. It helps to examine and study different gene expression patterns at other instances of time points. To monitor such gene expression distribution, the fuzzy clustering algorithm is used commonly. This paper proposes an assimilated form of fuzzy clustering with a Weighted Differential Evolution (WDE) optimization algorithm for clustering gene data. Moreover, WDE algorithm based fuzzy clustering can regress efficient evolutionary search procedures. Through the simulation study, it is observed that the WDE algorithm that has been proposed for solving numerical optimization problem can also cluster real-life gene expression data effectively. Different cluster validity indexes are used to validate clustering solutions. Comparative performance analysis and experiments are conducted on real-life datasets and gene expression datasets numerically and graphically.
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Das, R., Achom, A., Sharma, A., Mandal, J.K. (2022). A Novel Fuzzy Based Weighted Differential Evolution Algorithm for Clustering Gene Expression Data. In: Mandal, J.K., De, D. (eds) Advanced Techniques for IoT Applications. EAIT 2021. Lecture Notes in Networks and Systems, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-16-4435-1_26
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DOI: https://doi.org/10.1007/978-981-16-4435-1_26
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