Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415690 | Computational Statistics & Data Analysis | 2013 | 10 Pages |
Abstract
There is a vast variety of clustering methods available in the literature. The performance of many of them strongly depends on specific patterns in data. This paper introduces a clustering procedure based on the empirical likelihood method which inherits many advantages of the classical likelihood approach without imposing restrictive probability distribution constraints. The performance of the proposed procedure is illustrated on simulated and classification datasets with excellent results. The comparison of the algorithm with several well-known clustering methods is very encouraging. The procedure is more robust and has higher accuracy than the competitors.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Volodymyr Melnykov, Gang Shen,