A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data

作     者：Edwin Dhas, P. Sankara Gomathi, B. 

作者机构：Jayaraj Annapackiam CSI Coll Engn Dept Comp Sci & Engn Nazareth India Natl Engn Coll Dept Elect & Instrumentat Engn Kovilpatti India 

出 版 物：《JOURNAL OF SUPERCOMPUTING》 (超高速计算杂志)

年 卷 期：2020年第76卷第8期

页      面：5679-5693页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Microarray gene data Bio-inspired algorithm Clustering algorithm 

摘      要：The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the data into organized groups. Grouping similar gene expressions paves the way for effective analysis, and the relationship between the expressions can be figured out. Recognizing the benefits of clustering, this work intends to present a clustering algorithm by combining generalized hierarchical fuzzy C means (GHFCM) and grey wolf optimization (GWO) algorithms. The GWO algorithm is utilized for selecting the initial clustering point, and the GHFCM algorithm is employed for clustering the microarray gene data. The performance of the proposed clustering algorithm is tested with respect to precision, recall,F-measure and time consumption, and the results are compared with the existing approaches. The performance of the proposed work is satisfactory with betterF-measure rates and minimal time consumption.