Elephant search algorithm applied to data clustering
Deb, Suash; Tian, Zhonghuan; Fong, Simon; Wong, Raymond; Millham, Richard; Wong, Kelvin K. L.
Publication Place233 SPRING ST, NEW YORK, NY 10013 USA
AbstractData 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 BySCI ; CPCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000442576400009
The Source to ArticleWOS
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
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GB/T 7714
Deb, Suash,Tian, Zhonghuan,Fong, Simon,et al. Elephant search algorithm applied to data clustering[C]. 233 SPRING ST, NEW YORK, NY 10013 USA:SPRINGER,2018:6035-6046.
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