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Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms
Simon Fong1; Suash Deb2; Xin-She Yang3; Yan Zhuang1
2014-08-18
Source PublicationScientific World Journal
ISSN1537-744X
Volume2014
Abstract

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

DOIhttp://dx.doi.org/10.1155/2014/564829
URLView the original
Indexed BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000343587400001
PublisherHINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND
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Citation statistics
Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorSimon Fong
Affiliation1.Department of Computer and Information Science, University of Macau, Macau
2.Department of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi 835103, India
3.School of Science and Technology, Middlesex University, The Burroughs, London NW4 4BT, UK
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Suash Deb,Xin-She Yang,et al. Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms[J]. Scientific World Journal,2014,2014.
APA Simon Fong,Suash Deb,Xin-She Yang,&Yan Zhuang.(2014).Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms.Scientific World Journal,2014.
MLA Simon Fong,et al."Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms".Scientific World Journal 2014(2014).
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