A MULTI-OBJECTIVE GWO BASED CLUSTERING APPROACH FOR EFFICIENT DOCUMENT MINING

Abstract

Learning and data mining systems often use clustering. Such applications require efficient and fast clustering algorithms. However, clustering a dataset is often a difficult task, especially for complex datasets such as text classification. Clustering is a strategy of grouping data points based on their similarities. Data clustering can be done using any clustering algorithm. However, conventional partitioning clustering algorithms greatly depend on initial points and can drift towards inefficient clustering on improper initialization. Furthermore, these algorithms are less flexible for achieving multiple objectives required for clustering the complex overlapping classes. Therefore, in this work, we proposed a Gray Wolf Optimization (GWO) based multiobjective optimization algorithm for document clustering. GWO is a metaheuristic algorithm that works in the same way that grey wolves do when hunting prey. The proposed algorithm has the advantage of easily modifying clusters based on the objectives. The performance of the proposed algorithm on the Reuters-21578 dataset demonstrates that it outperforms k-means and affinity propagation algorithms.