Genetic Algorithm and Confusion Matrix for Document Clustering

Text mining is one of the most important tools in Information Retrieval. Text clustering is the process of classifying documents into predefined categories according to their content. Existing supervised learning algorithms to automatically classify text requires sufficient documentation to learn exactly. In this paper, Niching memetic algorithm and Genetic algorithm (GA) is presented in which feature selection an integral part of the global clustering search procedure that attempts to overcome the problem of finding optimal solutions at the local less promising in both clustering and feature selection. The concept of confusion matrix is then used for derivative works, and finally, hybrid GA is included for the final classification. Experimental results show benefits by using the proposed method which evaluates F-measure, purity and results better performance in terms of False positive, False negative, True positive and True negative.