Novel hybrid feature selection models for unsupervised document categorization

Dealing with high dimensional data is a challenging and computationally complex task in the data pre-processing phase of text clustering. Conventionally, union and intersection approaches have been used to combine results of different feature selection methods to optimize relevant feature space for document collection. Union method selects all features from considered sub-models, whereas, intersection method selects only common features identified by sub-models. However, in reality, any type of feature selection can cause a loss of some potentially important features. In this paper, a hybrid feature selection model called Modified Hybrid Union (MHU) is proposed, which selects features by considering the individual strengths and weaknesses of each constituent component of the model. A comparative evaluation of its performance for K-means clustering and Bio-inspired Flock-based clustering is also presented on standard data sets such as OWL-S TC and Reuters-21578.