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.
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