Elephant search algorithm on data clustering
Tian Z.1; Fong S.1; Wong R.3; Millham R.2
Source Publication2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
AbstractData clustering is one of the most popular branches in 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 it tries 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 too time consuming. 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 metaheuristic search method called Elephant Search Algorithm (ESA) is proposed to integrate into K-means, forming a new data clustering algorithm, namely C-ESA. The advantage of 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.
Keyworddata clustering elephant search algorithm meta-heuristic
URLView the original
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Durban University of Technology
3.University of New South Wales (UNSW) Australia
Recommended Citation
GB/T 7714
Tian Z.,Fong S.,Wong R.,et al. Elephant search algorithm on data clustering[C],2016:787-793.
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