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Efficient Clustering Approach using Statistical Method of Expectation-Maximization
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 46 - Number 12 |
| Year of Publication: 2012 |
| Authors: P.srinivasa Rao, K.sivarama Krishna, Nagesh Vadaparthi, S.vani Kumari |
10.5120/6958-9305
|
P.srinivasa Rao, K.sivarama Krishna, Nagesh Vadaparthi, S.vani Kumari . Efficient Clustering Approach using Statistical Method of Expectation-Maximization. International Journal of Computer Applications. 46, 12 ( May 2012), 1-7. DOI=10.5120/6958-9305
Abstract
Clustering is the activity of grouping objects in a dataset based on certain similarity. Available reports on clustering present several algorithms for obtaining effective clusters. Among the existing clustering techniques, hierarchical clustering is one of the widely preferred algorithms. Though there are many algorithms existing,K-Means for hierarchical clustering stand top. But still it is observed that the K-Means algorithm has number of limitations like initialization of parameters. To overcome this limitation, we propose the utilization of E-M algorithm. The K-Means algorithm is implemented by using measure of Cosine similarity and Expectation-Maximization(E-M) with Gaussian Mixture Model. The proposed method has two steps. In first step, the K-Means and E-M methods are combined to partition the input dataset into several smaller sub clusters. In the second step, sub clusters are merged continuously based on maximized Gaussian measure.
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