Semi supervised approach towards subspace clustering

High-dimensional data analysis is quite inevitable due to emerging technologies in various domains such as finance, healthcare, genomics and signal processing. Though data sets generated in these domains are high-dimensional, intrinsic dimensions that provide meaningful information are often much smaller. Conventionally, unsupervised clustering methods known as subspace clustering are utilized for finding clusters in different subspaces of high dimensional data, by identifying relevant features, irrespective of labels associated with each instance. Available label information, if incorporated in clustering algorithm, can bias the algorithm towards solutions more consistent with our knowledge, leading to improved cluster quality. Therefore, an Information Gain based Semi-supervised-subspace Clustering (IGSC) is proposed that identifies a subset of important attributes based on the known label for each data instance. The information about the labels associated with data sets is integrated with the search strategy for subspaces to leverage them into a model based clustering approach. Our experimentation on 13 real world labeled data sets proves the feasibility of IGSC and we validate the clusters obtained, using an improvised Davies Bouldin Index (DBI) for semi-supervised clusters.