Proprties of biclustering algorithms and a novel biclustering technique based on relative density

Biclustering is found to be useful in areas like data mining and bioinformatics. The term biclustering involves searching subsets of observations and features forming coherent structure. This can be interpreted in different ways like spatial closeness, relation between features for selected observations etc. This paper discusses different properties, objectives and approaches of biclustering algorithms. We also present an algorithm which detects feature relation based biclusters using density based techniques. Here we use relative density of regions to identify biclusters embedded in the data. Properties of this algorithm are discussed and demonstrated using artificial datasets. Proposed method is seen to give better results on these datasets using paired right tailed t test. Usefulness of proposed method is also demonstrated using real life datasets.