A Genetic Evolutionary ROCK Algorithm

摘要：

In this paper, we propose a genetic evolutionary ROCK algorithm (GE-ROCK). GE-ROCK is an improved ROCK algorithm which combines the techniques of clustering and genetic optimization. Genetic optimization is exploited here to improve the clustering process. In GE-ROCK, similarity function is used throughout the iterative clustering process, while in the conventional ROCK algorithm, similarity function is only to be used for the initial calculation. To evaluate the performance of the GE-ROCK, we exploit the well-known voting data sets. A comparative analysis demonstrates that the GE-ROCK leads to the superior performance not only better clustering quality but also shorter computing time when comparing the ROCK algorithm commonly used in the literature.

关键词： clustering ROCK algorithm genetic optimization

作者: Qiongbing Zhang Lixin Ding Shanshan Zhang

作者单位: State key Laboratory of Software Engineering State key Laboratory of Software Engineering Wuhan University, 129 Luoyu Road Wuhan, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 347-351

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）