BiSim: A Simple and Efficient Biclustering Algorithm

Analysis of gene expression data includes classification of the data into groups and subgroups based on similar expression patterns. Standard clustering methods for the analysis of gene expression data only identifies the global models while missing the local expression patterns. In order to identify the missed patterns biclustering approach has been introduced. Various biclustering algorithms have been proposed by scientists. Among them binary inclusion maximal algorithm (BiMax) forms biclusters when applied on a gene expression data through divide and conquer approach. The worst-case running-time complexity of BiMax for matrices containing disjoint biclusters is O(nmb) and for arbitrary matrices is of order O(nmb min{n, m}) where b is the number of all inclusion-maximal biclusters in matrix. In this paper we present an improved algorithm, BiSim, for biclustering which is easy and avoids complex computations as in BiMax. The complexity of our approach is O(n*m) for n genes and m conditions, i.e, a matrix of size n*m. Also it avoids extra computations within the same complexity class and avoids missing of any biclusters.