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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Vydáno v:2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) s. 787 - 793
Hlavní autoři: Zhonghuan Tian, Fong, Simon, Wong, Raymond, Millham, Richard
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2016
Témata:
Algorithm design and analysis
Classification algorithms
Clustering algorithms
data clustering
Electronic mail
elephant search algorithm
Heuristic algorithms
meta-heuristic
Partitioning algorithms
Statistics
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Shrnutí: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