Elephant search algorithm on data clustering

Data 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...

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Bibliographic Details
Published in:	2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) pp. 787 - 793
Main Authors:	Zhonghuan Tian, Fong, Simon, Wong, Raymond, Millham, Richard
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2016
Subjects:	Algorithm design and analysis Classification algorithms Clustering algorithms data clustering Electronic mail elephant search algorithm Heuristic algorithms meta-heuristic Partitioning algorithms Statistics
Online Access:	Get full text
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Summary:	Data 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.
DOI:	10.1109/FSKD.2016.7603276