Unsupervised feature selection technique based on genetic algorithm for improving the Text Clustering

The genetic algorithm (GA) is proposed to solve the unsupervised feature selection problem, namely, (FSGATC), which is used to create a new subset of informative features in order to obtain more accurate clusters.

Abstract

The increasing amount of text documents in digital forms affect the text analysis techniques. Text clustering (TC) is one of the important techniques used for showing a massive amount of text documents by clusters. Hence, the main problem that affects the text clustering technique is the presence sparse and uninformative features on the text documents. The feature selection (FS) is an essential unsupervised learning technique. This technique is used to select informative features to improve the performance of text clustering algorithm. Recently, the meta-heuristic algorithms are successfully applied to solve several hard optimization problems. In this paper, we proposed the genetic algorithm (GA) to solve the unsupervised feature selection problem, namely, (FSGATC). This method is used to create a new subset of informative features in order to obtain more accurate clusters. Experiments were conducted using four benchmark text datasets with variant characteristics. The results showed that the proposed FSGATC is improved the performance of the text clustering algorithm and got better results compared with k-mean clustering standalone. Finally, the proposed method "FSGATC" evaluated by F-measure and Accuracy, which are common measures used in the domain of text clustering.