{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T23:34:20Z","timestamp":1723160060906},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2016,5,16]],"date-time":"2016-05-16T00:00:00Z","timestamp":1463356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61175056, 61402070"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["2015020023"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"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.<\/jats:p>","DOI":"10.3390\/e18050185","type":"journal-article","created":{"date-parts":[[2016,5,17]],"date-time":"2016-05-17T14:20:11Z","timestamp":1463494811000},"page":"185","source":"Crossref","is-referenced-by-count":8,"title":["An Information Entropy-Based Animal Migration Optimization Algorithm for Data Clustering"],"prefix":"10.3390","volume":"18","author":[{"given":"Lei","family":"Hou","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Jian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5848-6398","authenticated-orcid":false,"given":"Rong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: A review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comput. 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