Adaptive optimized clustering for Veterans' Administration Lung Cancer
Khaled Mohammed Fouad, Mohamed F. Dawood · 2016
Lung cancer is considered as the leading reason of death for men and women. The clustering establishes the clusters from dataset of veterans' lung cancer based on a similarity measure to discover a new set of categories. K-Means, which is used in clustering, owns certain drawbacks such as determining number of clusters being used and determining a well-defined centroid. In this paper, a proposed algorithm called PSO-AKMeans, which is used in Veterans' Administration Lung Cancer (VALC) domain, is presented and evaluated. PSO-AKMeans uses the adaptation concept in AK-Means algorithm and particle swarm optimization to optimize total number of clusters and clusters' centroid which are used in clustering process. The experiment result proves that PSO-AKMeans provides better performance, higher accuracy and lower execution time than other traditional clustering algorithms in VALC domain.