Symmetry Based Automatic Evolution of Clusters: A New Approach to Data Clustering.
We present a multiobjective genetic clustering approach, in which data points are assigned to clusters based on new line symmetry distance. The proposed algorithm is called multiobjective line symmetry based genetic clustering (MOLGC). Two objective functions, first the Davies-Bouldin (DB) index and second the line symmetry distance based objective functions, are used. The proposed algorithm evolves near-optimal clustering solutions using multiple clustering criteria, without a priori knowledge of the actual number of clusters. The multiple randomized K dimensional (Kd) trees based nearest neighbor search is used to reduce the complexity of finding the closest symmetric points. Experimental results based on several artificial and real data sets show that proposed clustering algorithm can obtain optimal clustering solutions in terms of different cluster quality measures in comparison to existing SBKM and MOCK clustering algorithms.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, Mody University of Science and Technology, Lakshmangarh, Rajasthan 332311, India.
Singh
S
Vijendra
School of Computer Engineering, KIIT University, Bhubaneswar 751024, India.
Sahoo
S
Laxman
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular