Implementation of Novel Fuzzy C-Means Method in Gene Data
K. Uma Maheswari1, Jeevaa Katiravan2

1K. Uma Maheswari, Research Scholar, Dept. of Computer Science & Engineering, Sri SatyaSai University of Technology & Medical Sciences, Sehore, Bhopal-Indore Road, Madhya Pradesh, India.
2Jeevaa Katiravan, Research Guide, Dept. of Computer Science & Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Bhopal Indore Road, Madhya Pradesh, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5765-5767 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7299038620/2020©BEIESP | DOI: 10.35940/ijrte.F7299.038620

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Abstract: Microarray innovation as of late has significant effects in numerous fields, for example, medical fields, bio-drug, describing different gene capacities, understanding diverse atomic bio-legitimate procedures, gene expression profiling and so on. In any case, microarray chips comprise of expression levels of an immense number of genes, thus produce huge measures of data to deal with. Because of its huge volume, the computational examination is basic for extricating information from microarray gene expression data. Clustering is one of the essential ways to deal with break down such a huge measure of data to find the gatherings of co-communicated genes. The issues tended to in hard clustering could be fathomed in a fuzzy clustering strategy. Among fuzzy based clustering, fuzzy c-means (FCM) is the most reasonable for microarray gene expression data. The issue related to fuzzy c-means is the number of clusters to be generated for the given dataset should be determined in earlier. The fundamental goal of this proposed Novel fuzzy c-means (NFCM) strategy is to decide the exact number of clusters and decipher the equivalent effect.
Keywords: NFCM, Gene Data, Fuzzy C-Means..
Scope of the Article: Fuzzy logics.