Open AccessImplementation of Novel Fuzzy C-Means Method in Gene Data
Author(s) -
K. Uma Maheswari,
Jeevaa Katiravan
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f7299.038620
Subject(s) - fuzzy logic , data mining , cluster analysis , decipher , microarray analysis techniques , computer science , fuzzy clustering , gene expression profiling , microarray databases , gene chip analysis , profiling (computer programming) , microarray , expression (computer science) , computational biology , gene , bioinformatics , gene expression , artificial intelligence , biology , genetics , programming language , operating system
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.