Authors: Z.H. Che
Addresses: Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 10608, Taiwan
Abstract: The fuzzy C-means (FCM) algorithm is a commonly used fuzzy clustering method which conducts data clustering by randomly selecting initial centroids. With larger data size or attribute dimensions, clustering results may be affected and more repetitive computations are required. To compensate the effect of random initial centroids on results, this study proposed a hybrid algorithm immune genetic annealing fuzzy C-means algorithm (IGAFA). This algorithm obtains the proper initial cluster centroids to improve clustering efficiency and then tests them through three data sets: Hamberman's survival, iris, and liver disorders, and compares the results with the executed results of genetic fuzzy C-means algorithm (GFA), immune fuzzy C-means algorithm (IFA), and annealing fuzzy C-means algorithm (AFA). The results suggest that IGAFA could achieve better clustering results. [Received: November 18, 2009; Accepted: July 19, 2010]
Keywords: fuzzy clustering; fuzzy C-means; FCM; clustering efficiency; genetic algorithms; artificial immune systems; simulated annealing; liver disorders; patient survival; iris flowers.
European Journal of Industrial Engineering, 2012 Vol.6 No.1, pp.50 - 67
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 08 Jan 2012 *