Mohammed, Athraa Jasim and Yusof, Yuhanis and Husni, Husniza (2016) Integrated bisect K-means and firefly algorithm for hierarchical text clustering. Journal of Engineering and Applied Sciences, 11 (3). pp. 522-527. ISSN 1816-949X

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Abstract

Hierarchical text clustering plays a significant role in systematically browsing, summarizing and organizing documents into structure manner. However, the Bisect K-means which is a well-known hierarchical clustering algorithm is only able to generate local optimal solutions due to the employment of K-means as part of its process. In this study, we propose to replace the K-means with firefly algorithm, hence producing a Bisect FA for hierarchical clustering. At each level of the proposed Bisect FA, firefly algorithm works to produce the best clusters. For evaluation purposes, we performed experiments on 20 newsgroups dataset that is commonly used in text clustering studies.The results demonstrate that Bisect FA obtains more accurate and compact clustering than Bisect K-means, K-means and C-firefly algorithms. Such a result indicates that the proposed Bisect FA is a competitive algorithm for unsupervised learning.

Item Type:	Article
Uncontrolled Keywords:	Hierarchical Text Clustering, firefly algorithm, bisect K-means divisive clustering, documents
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	18 Jan 2017 03:34
Last Modified:	18 Jan 2017 03:34
URI:	https://repo.uum.edu.my/id/eprint/20644

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