Research Area:  Metaheuristic Computing

Abstract:

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.

Keywords:  
text clustering
efficient
new swarm-based optimization
exploration
exploitation
Computational Intelligence
accuracy
precision
recall
F-measure
purity
entropy

Author(s) Name:  Laith Mohammad Abualigah, Ahamad Tajudin Khader & Essam Said Hanandeh

Publisher name:  Springer

DOI:  10.1007/s10489-018-1190-6

Volume Information:  48, pages 4047–4071

Paper Link:   https://link.springer.com/article/10.1007/s10489-018-1190-6