Abstract
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 article 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. The proposed method is seen to provide better results on both artificial and real datasets. Paired right tailed t test is used for artificial datasets.
Abstract (translated by Google)
URL
http://arxiv.org/abs/1811.04661