Data clustering using multivariant optimization algorithm

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• 作者：Qin-Hu Zhang ; Bao-Lei Li ; Ya-Jie Liu…
• 关键词：Data clustering ; Cluster centers ; Multivariant optimization algorithm ; Global and local optimization
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：7
• 期：5
• 页码：773-782
• 全文大小：592 KB
• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X
• 卷排序：7

文摘

Data clustering is one of the most popular techniques in data mining to group data with great similarity and high dissimilarity into each cluster. This paper presents a new clustering method based on a novel heuristic optimization algorithm proposed recently and named as multivariant optimization algorithm (MOA) to locate the optimal solution automatically through global and local alternating search implemented by a global exploration group and several local exploitation groups. In order to demonstrate the performance of MOA-clustering method, it is applied to group six real-life datasets to obtain their clustering results, which may be compared with those received by employing K-means algorithm, genetic algorithm and particle swarm optimization. The results show that the proposed clustering algorithm is an effective and feasible method to reach a high accurate rate and stability in clustering problems.