Dynamic clustering using combinatorial particle swarm optimization
Dynamic clustering using combinatorial particle swarm optimization
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DOI：
10.1007/s10489-012-0373-9
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发表时间：
2013-04-01
影响因子：
5.3
通讯作者：
Hasheminejad, Seyed Mohammad Hossein
中科院分区：
文献类型：
--
作者：
Masoud, Hamid;Jalili, Saeed;Hasheminejad, Seyed Mohammad Hossein
组合粒子群算法(CPSO)是一种求解组合优化问题的较新技术。粒子群优化算法已被应用于不同的应用领域，如分区聚类、项目调度等，并表现出了很好的性能。在分区聚类问题中，粒子群优化算法需要预先确定聚类个数。然而，在许多聚类问题中，正确的聚类个数是未知的，而且通常是不可能估计的。本文提出了一种改进的动态聚类算法CPSOII，该算法自动寻找最佳聚类个数，同时对数据对象进行分类。CPSOII使用重新编号过程作为预处理步骤，并使用几个扩展的PSO算子来增加种群多样性和去除冗余粒子。使用重编号过程提高了种群的多样性、收敛速度和解的质量。在绩效评估方面，我们使用人工数据和真实数据对CPSOII进行了检验。实验结果表明，CPSOII算法是非常有效、健壮的，能够成功地解决已知和未知聚类个数的聚类问题。将CPSOII算法的结果与CPSO算法以及KCPSO、CGA和K-Means等其他聚类技术的结果进行了比较，发现CPSOII算法得到了令人满意的结果。例如，它提高了DBI标准对HEPATO数据集的9.26%的值。
Combinatorial Particle Swarm Optimization (CPSO) is a relatively recent technique for solving combinatorial optimization problems. CPSO has been used in different applications, e.g., partitional clustering and project scheduling problems, and it has shown a very good performance. In partitional clustering problem, CPSO needs to determine the number of clusters in advance. However, in many clustering problems, the correct number of clusters is unknown, and it is usually impossible to estimate. In this paper, an improved version, called CPSOII, is proposed as a dynamic clustering algorithm, which automatically finds the best number of clusters and simultaneously categorizes data objects. CPSOII uses a renumbering procedure as a preprocessing step and several extended PSO operators to increase population diversity and remove redundant particles. Using the renumbering procedure increases the diversity of population, speed of convergence and quality of solutions. For performance evaluation, we have examined CPSOII using both artificial and real data. Experimental results show that CPSOII is very effective, robust and can solve clustering problems successfully with both known and unknown number of clusters. Comparing the obtained results from CPSOII with CPSO and other clustering techniques such as KCPSO, CGA and K-means reveals that CPSOII yields promising results. For example, it improves 9.26 % of the value of DBI criterion for Hepato data set.