Title :
Empirical evaluation of K-Means, Bisecting K-Means, Fuzzy C-Means and Genetic K-Means clustering algorithms
Author :
Shreya Banerjee;Ankit Choudhary;Somnath Pal
Author_Institution :
Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India
Abstract :
Clustering is one of the most widely studied problem in machine learning and data mining. The algorithms for clustering depend on the application scenario and data domain. K-Means algorithm is one of the most popular clustering techniques that depend on distance measure. In this work, an extensive empirical evaluation of three significant variations of K-Means algorithm is carried out on the basis of six internal and external validity indices. It has been seen that performance of K-Means and Bisecting K-Means are similar, while Fuzzy C-Means gives better performance and Genetic K-Means performs the best. On the light of empirical result obtained in this paper, method for further improvement of the performance of Genetic K-Means is suggested.
Keywords :
"Clustering algorithms","Partitioning algorithms","Indexes","Algorithm design and analysis","Genetics","Machine learning algorithms","Genetic algorithms"
Conference_Titel :
Electrical and Computer Engineering (WIECON-ECE), 2015 IEEE International WIE Conference on
DOI :
10.1109/WIECON-ECE.2015.7443889