Yeh Wei-Chang, Lai Chyh-Ming
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu City, Taiwan.
PLoS One. 2015 Sep 8;10(9):e0137246. doi: 10.1371/journal.pone.0137246. eCollection 2015.
Data clustering is commonly employed in many disciplines. The aim of clustering is to partition a set of data into clusters, in which objects within the same cluster are similar and dissimilar to other objects that belong to different clusters. Over the past decade, the evolutionary algorithm has been commonly used to solve clustering problems. This study presents a novel algorithm based on simplified swarm optimization, an emerging population-based stochastic optimization approach with the advantages of simplicity, efficiency, and flexibility. This approach combines variable vibrating search (VVS) and rapid centralized strategy (RCS) in dealing with clustering problem. VVS is an exploitation search scheme that can refine the quality of solutions by searching the extreme points nearby the global best position. RCS is developed to accelerate the convergence rate of the algorithm by using the arithmetic average. To empirically evaluate the performance of the proposed algorithm, experiments are examined using 12 benchmark datasets, and corresponding results are compared with recent works. Results of statistical analysis indicate that the proposed algorithm is competitive in terms of the quality of solutions.
数据聚类在许多学科中都有广泛应用。聚类的目的是将一组数据划分为多个簇，同一簇内的对象彼此相似，而与属于不同簇的其他对象不同。在过去十年中，进化算法已被广泛用于解决聚类问题。本研究提出了一种基于简化群优化的新算法，这是一种新兴的基于种群的随机优化方法，具有简单、高效和灵活的优点。该方法在处理聚类问题时结合了可变振动搜索（VVS）和快速集中策略（RCS）。VVS是一种探索性搜索方案，通过搜索全局最优位置附近的极值点来提高解的质量。RCS则是通过使用算术平均值来加快算法的收敛速度。为了实证评估所提算法的性能，使用12个基准数据集进行了实验，并将相应结果与近期研究进行了比较。统计分析结果表明，所提算法在解的质量方面具有竞争力。
