title: A heuristic method for finding the optimal number of clusters with application in medical data.
abstract: In this paper, a heuristic method for determining the optimal number of clusters is proposed. Four clustering algorithms, namely K-means, Growing Neural Gas, Simulated Annealing based technique, and Fuzzy C-means in conjunction with three well known cluster validity indices, namely Davies-Bouldin index, Calinski-Harabasz index, Maulik-Bandyopadhyay index, in addition to the proposed index are used. Our simulations evaluate capability of mentioned indices in some artificial and medical datasets.
authors.affiliation: Biological Signal and Image Processing Laboratory (BiSIPL), Department of Electrical Engineering, Sharif University of Technology, Iran.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Bayati
authors.forename: Heydar
authors.initials: H
authors.lastname: Davoudi
authors.forename: Emad
authors.initials: E
authors.lastname: Fatemizadeh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D017143:Fuzzy Logic; D020869:Gene Expression Profiling; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods