Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.
Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.
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DOI：
10.1371/journal.pone.0130995
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Alrefaei M
中科院分区：
文献类型：
--
作者：
Abubaker A;Baharum A;Alrefaei M
提出了一种基于多目标粒子群优化和模拟退火的自动聚类算法MOPSOSA。所提出的算法是能够自动聚类，这是适当的分区数据集到一个合适的数量的集群。MOPSOSA算法结合了多目标粒子群优化算法和多目标模拟退火算法的特点。同时优化了三个聚类有效性指标，以建立合适的聚类数和适当的聚类数据集。第一个聚类有效性指标以欧氏距离为中心，第二个聚类有效性指标以点对称距离为中心，最后一个聚类有效性指标以短距离为中心。一些算法已被比较与MOPSOSA算法在解决聚类问题，通过确定实际的聚类数和最佳聚类。计算实验进行了研究14个人工和5个真实的生活数据集。
This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.
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