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An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering

by Lei Hou 1, Jian Gao 1,2,* and Rong Chen 1,*
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Authors to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2016, 18(5), 185;
Received: 16 March 2016 / Revised: 3 May 2016 / Accepted: 10 May 2016 / Published: 16 May 2016
(This article belongs to the Section Information Theory, Probability and Statistics)
Data clustering is useful in a wide range of application areas. The Animal Migration Optimization (AMO) algorithm is one of the recently introduced swarm-based algorithms, which has demonstrated good performances for solving numeric optimization problems. In this paper, we presented a modified AMO algorithm with an entropy-based heuristic strategy for data clustering. The main contribution is that we calculate the information entropy of each attribute for a given data set and propose an adaptive strategy that can automatically balance convergence speed and global search efforts according to its entropy in both migration and updating steps. A series of well-known benchmark clustering problems are employed to evaluate the performance of our approach. We compare experimental results with k-means, Artificial Bee Colony (ABC), AMO, and the state-of-the-art algorithms for clustering and show that the proposed AMO algorithm generally performs better than the compared algorithms on the considered clustering problems. View Full-Text
Keywords: animal migration optimization; information entropy; data clustering animal migration optimization; information entropy; data clustering
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Hou, L.; Gao, J.; Chen, R. An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering. Entropy 2016, 18, 185.

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