A genetic evolutionary ROCK algorithm
Qiongbing Zhang, Lixin Ding, Shanshan Zhang · 2010
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.