Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering

Text clustering is suitable for dividing many text documents into distinct groups. The size of the documents has an impact on the performance of text clustering, reducing its effectiveness. Text documents often include sparse and uninformative characteristics, which can negatively impact the efficiency of the text clustering technique and increase the computational time required. Feature selection is a crucial strategy in unsupervised learning that involves choosing a subset of informative text features to enhance the efficiency of text clustering and decrease computing time. This work presents a novel approach based on an improved Prairie dog algorithm to solve the feature selection problem. K-means clustering is employed to assess the efficacy of the acquired subgroups of features. The proposed algorithm is being compared to other algorithms published in the literature. The feature selection strategy ultimately promotes the clustering algorithm to get precise clusters.