Affiliated with RCfalse
Elephant search algorithm applied to data clustering
Suash Deb1; Zhonghuan Tian2; Simon Fong2; Raymond Wong3; Richard Millham4; Kelvin K. L. Wong5,6
Source PublicationSoft Computing

Data clustering is one of the most popular branches of machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and then it attempts to improve the partitions progressively. Different initial partitions can result in different final clusters. Trying through all the possible candidate clusters for the perfect result is computationally expensive. Meta-heuristic algorithm aims to search for global optimum in high-dimensional problems. Meta-heuristic algorithm has been successfully implemented on data clustering problems seeking a near optimal solution in terms of quality of the resultant clusters. In this paper, a new meta-heuristic search method named elephant search algorithm (ESA) is proposed to integrate into K-means, forming a new data clustering algorithm, namely C-ESA. The advantage of C-ESA is its dual features of (i) evolutionary operations and (ii) balance of local intensification and global exploration. The results by C-ESA are compared with classical clustering algorithms including K-means, DBSCAN, and GMM-EM. C-ESA is shown to outperform the other algorithms in terms of clustering accuracy via a computer simulation. C-ESA is also implemented on time series clustering compared with classical algorithms K-means, Fuzzy C-means and classical meta-heuristic algorithm PSO. C-ESA outperforms the other algorithms in term of clustering accuracy. C-ESA is still comparable compared with state of art time series clustering algorithm K-shape.

KeywordData Clustering Elephant Search Algorithm Meta-heuristic Time Series Clustering
URLView the original
Indexed BySCIE ; CPCI-S
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000442576400009
Fulltext Access
Citation statistics
Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorKelvin K. L. Wong
Affiliation1.IT and Educational Consultant, Ranchi, Jharkhand, India
2.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China
3.School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
4.Department of Information Technology, Durban University of Technology, Durban, South Africa
5.Centre for Biomedical Engineering, Department of Electronic and Electrical Engineering, University of Adelaide, Adelaide, Australia
6.School of Medicine, University of Western Sydney, Campbelltown, Sydney, Australia
Recommended Citation
GB/T 7714
Suash Deb,Zhonghuan Tian,Simon Fong,et al. Elephant search algorithm applied to data clustering[J]. Soft Computing,2018,22(18):6035-6046.
APA Suash Deb,Zhonghuan Tian,Simon Fong,Raymond Wong,Richard Millham,&Kelvin K. L. Wong.(2018).Elephant search algorithm applied to data clustering.Soft Computing,22(18),6035-6046.
MLA Suash Deb,et al."Elephant search algorithm applied to data clustering".Soft Computing 22.18(2018):6035-6046.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Suash Deb]'s Articles
[Zhonghuan Tian]'s Articles
[Simon Fong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Suash Deb]'s Articles
[Zhonghuan Tian]'s Articles
[Simon Fong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Suash Deb]'s Articles
[Zhonghuan Tian]'s Articles
[Simon Fong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.