Unsupervised text feature selection technique based on particle swarm optimization algorithm for improving the text clustering

After incensing the amount of text information on internet web pages, the dealing with this information is very complex due to the volume of information. Text clustering technique is an appropriate task to deal with a huge amount of text documents by grouping set of documents into groups. Text documents contain uninformative features, which decrease the performance of the text clustering technique. Feature selection is an unsupervised technique used to select informative features by creating a new subset of informative features. This technique used to improve the performance of the underlying algorithm. Latterly, several complex optimization problems are success solved by meta- heuristic algorithms. In this paper, we proposed the Particle swarm optimization algorithm to solve the feature selection problem, namely, (FSPSOTC). The feature selection technique encourages the k-mean text clustering technique to obtain more accurate clusters. Experiments were conducted using four standard benchmark text datasets with different characteris-tics. Experimental results showed that the proposed method (FSPSOTC) is enhanced the performance of the text clustering technique by dealing with a new subset of informative features.