Link based BPSO for feature selection in big data text clustering

Feature selection is a significant task in data mining and machine learning applications which eliminates irrelevant and redundant features and improves learning performance. This paper proposes a new feature selection method for unsupervised text clustering named link based particle swarm optimization (LBPSO). This method introduces a new neighbour selection strategy in BPSO to select prominent features. The performance of traditional particle swarm optimization(PSO)is limited by using global best updating mechanism for feature selection. Instead of using global best, LBPSO particles are updated based on neighbour best position to enhance the exploitation and exploration capability. These prominent features are then tested using k-means clustering algorithm to improve the performance and reduce the cost of computational time of the proposed algorithm. The performance of LBPSO are investigated on three published datasets, namely Reuter 21578, TDT2 and Tr11. Our results based on evaluation measures show that the performance of LBPSO is superior than other PSO based algorithms.