A new feature selection method to improve the document clustering using particle swarm optimization algorithm

The large amount of text information on the Internet and in modern applications makes dealing with this volume of information complicated. The text clustering technique is an appropriate tool to deal with an enormous amount of text documents by grouping these documents into coherent groups. The document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative features (i.e., noisy, irrelevant, and unnecessary features), which affect the effectiveness of the text clustering technique. The feature selection technique is a primary unsupervised learning method employed to select the informative text features to create a new subset of a document's features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems have been successfully solved using metaheuristic algorithms. This paper proposes a novel feature selection method, namely, feature selection method using the particle swarm optimization (PSO) algorithm (FSPSOTC) to solve the feature selection problem by creating a new subset of informative text features. This new subset of features can improve the performance of the text clustering technique and reduce the computational time. Experiments were conducted using six standard text datasets with several characteristics. These datasets are commonly used in the domain of the text clustering. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with the other well-known algorithms i.e., feature selection method using a genetic algorithm to improve the text clustering (FSGATC), and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection.