Optimal cluster analysis using hybrid K-Means and Ant Lion Optimizer

K-Means is a popular cluster analysis method which aims to partition a number of data points into K clusters. It has been successfully applied to a number of problems. However, the efficiency of K-Means depends on its initialization of cluster centers. Different swarm intelligence techniques are app...

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Veröffentlicht in:Karbala International Journal of Modern Science Jg. 4; H. 4; S. 347 - 360
Hauptverfasser: Majhi, Santosh Kumar, Biswal, Shubhra
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2018
Schlagworte:
Ant Lion Optimization
Clustering
K-Means
Statistical analysis
Stochastic global optimization model
Stochastic global optimization model
Statistical analysis
Clustering
K-Means
Ant Lion Optimization
ISSN:2405-609X
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Zusammenfassung:K-Means is a popular cluster analysis method which aims to partition a number of data points into K clusters. It has been successfully applied to a number of problems. However, the efficiency of K-Means depends on its initialization of cluster centers. Different swarm intelligence techniques are applied to clustering problem for enhancing the performance. In this work a hybrid clustering approach based on K-means and Ant Lion Optimization has been considered for optimal cluster analysis. Ant Lion Optimization (ALO) is a stochastic global optimization model. The performance of the proposed algorithm is compared against the performance of K-Means, KMeans-PSO, KMeans-FA, DBSCAN and Revised DBSCAN clustering methods based on different performance metrics. Experimentation is performed on eight datasets, for which the statistical analysis is carried out. The obtained results indicate that the hybrid of K-Means and Ant Lion Optimization method performs preferably better than the other three algorithms in terms of sum of intracluster distances and F-measure.
ISSN:2405-609X
DOI:10.1016/j.kijoms.2018.09.001