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Basic firefly algorithm for document clustering

Mohammed, Athraa Jasim and Yusof, Yuhanis and Husni, Husniza (2015) Basic firefly algorithm for document clustering. In: 2nd Innovation and Analytics Conference and Exhibition (IACE 2015), 29 September–1 October 2015, Kedah, Malaysia.

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The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process.To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simplicity, such an approach tends to be trapped in a local minimum during its search for an optimal solution. To address the shortcoming, this paper proposes a Basic Firefly (Basic FA) algorithm to cluster text documents.The algorithm employs the Average Distance to Document Centroid (ADDC) as the objective function of the search. Experiments utilizing the proposed algorithm were conducted on the 20 News groups benchmark dataset. Results demonstrate that the Basic FA generates a more robust and compact clusters than the ones produced by K-means and Particle Swarm Optimization (PSO).

Item Type: Conference or Workshop Item (Paper)
Additional Information: AIP Conference Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Dr. Yuhanis Yusof
Date Deposited: 28 Jun 2016 03:38
Last Modified: 28 Jun 2016 03:38
URI: http://repo.uum.edu.my/id/eprint/18306

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