Laith Mohammad Abualigah*, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed A. Otair and Shishir K. Shandilya Pages 1 - 11 ( 11 )
Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Discussion: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and k-mean techniques. Coclusion: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem.
Text Document Clustering Problem, β-Hill Climbing, Local Exploitation Search, Optimization Problem, Clusters.
Faculty of Computer Sciences and Informatics, Amman Arab University, Amman - 11953, Jordan; School of Computer Science, Universiti Sains Malaysia, Penang, Department of Computer Information System, Zarqa University, P.O. Box 13132, Zarqa, School of Computer Science, Universiti Sains Malaysia, Penang, Faculty of Computer Sciences and Informatics, Amman Arab University, Amman - 11953, Department of Computer Science & Engineering, NRI Institute of Information Science and Technology, Bhopal