@inproceedings{49db245aaa7e452091752090162186c1,
title = "Biogeography-based optimization for cluster analysis",
abstract = "With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis.",
keywords = "Biogeography-based optimization, Cluster analysis, Genetic algorithm",
author = "Xueyan Wu and Hainan Wang and Zhimin Chen and Zhihai Lu and Preetha Phillips and Shuihua Wang and Yudong Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; International Conference on Computer, Communication and Computational Sciences, ICCCCS 2016 ; Conference date: 12-08-2016 Through 13-08-2016",
year = "2017",
doi = "10.1007/978-981-10-3770-2_1",
language = "English",
isbn = "9789811037696",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "3--12",
editor = "Singh, {Vivek Kumar} and Shailesh Tiwari and Mishra, {Krishn K.} and Bhatia, {Sanjiv K.}",
booktitle = "Advances in Computer and Computational Sciences - Proceedings of ICCCCS 2016",
}