Title

Authors

Affiliation

¹Department of Computer Science and Engineering, National Institute of Technology Calicut, India- 673 601; ²Information Technology and Systems Area, Indian Institute of Management Kozhikode, India- 673 570

Email

nazeer@nitc.ac.in; *Corresponding authors

Article Type

Hypothesis

Date

Received December 18, 2012; Accepted December 21, 2012; Published January 18, 2013

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.

Citation

Nazeer  et al.   Bioinformation 9(2): 084-088 (2013)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.