An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

Author(s): Laith Mohammad Abualigah*, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed Abdallh Otair, Shishir Kumar Shandilya.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 4 , 2020

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Abstract:

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.

Aims: 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.

Methods: 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 kmean techniques.

Results: 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.

Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

Keywords: Text clustering, β-Hill climbing, local exploitation search, optimization problem, clusters, k-mean techniques.

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Article Details

VOLUME: 16
ISSUE: 4
Year: 2020
Page: [296 - 306]
Pages: 11
DOI: 10.2174/1573405614666180903112541
Price: $65

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