局部硬聚类的进化和群体智能方法

J. Prakash, P. Singh
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引用次数: 8

摘要

聚类是一种无监督的分类方法,它根据一些相似度度量对未标记数据集中的对象进行分类。传统的分割聚类算法,如K-Means、K-Medoids等,存在最终解依赖于初始解,容易陷入局部最优的缺点。基于自然启发的种群全局搜索优化方法具有候选解迭代上阶、去中心化、并行性和自组织行为等关键特性,能够更有效地克服传统分区聚类方法的不足。在这项工作中,我们比较了广泛应用的进化算法,即遗传算法(GA)和差分进化(DE),以及群体智能方法,即粒子群优化(PSO)和人工蜂群(ABC)的性能,通过内部有效性标准来评估聚类的质量,平方误差和(SSE),这是基于聚类的紧密性。基于三个真实数据集和一个合成数据集,对广泛的结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolutionary and Swarm Intelligence Methods for Partitional Hard Clustering
Clustering is an unsupervised classification method where objects in the unlabeled data set are classified on the basis of some similarity measure. The conventional partitional clustering algorithms, e.g., K-Means, K-Medoids have several disadvantages such as the final solution is dependent on initial solution, they easily stuck into local optima. The nature inspired population based global search optimization methods offer to be more effective to overcome the deficiencies of the conventional partitional clustering methods as they possess several desired key features like up gradation of the candidate solutions iteratively, decentralization, parallel nature, and self organizing behavior. In this work, we compare the performance of widely applied evolutionary algorithms namely Genetic Algorithm (GA) and Differential Evolution (DE), and swarm intelligence methods namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) to find the clustering solutions by evaluating the quality of cluster with internal validity criteria, Sum of Square Error (SSE), which is based on compactness of cluster. Extensive results are compared based on three real and one synthetic data sets.
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