Bicluster detection using strength pareto front evolutionary algorithm

Maryam Golchin, Alan Wee-Chung Liew

Abstract: Biclustering has many applications in various fields such as pattern classification, information retrieval, data mining and functional annotation. Biclustering extracts accurate information from gene expression datasets by clustering rows and columns of a dataset simultaneously. In this paper, a new multi-objective evolutionary biclustering framework based on strength Pareto front evolutionary algorithm (SPEA2) is proposed. A heuristic search is added into SPEA2 to delete and add genes and conditions into randomly generated biclusters. In order to select the best bicluster among Pareto front solutions, k-mean algorithm is used. The new population is generated based on mutation and crossover. The performance of the proposed method is evaluated using synthetic and real datasets and compared with several well-known biclustering methods. The experimental results show better performance and significant enrichment of detected biclusters.

External IDs:dblp:conf/acsc/GolchinL16