Abstract: With the appearance of massive big data, some new parallel computing models have been proposed for clustering algorithm. A dynamic gemini population based differential evolution K-mediods clustering algorithm called DGP-DE-K-mediods in cloud environments is proposed in this paper. In DGP-DE-K-mediods, gemini population scheme is adopted to improve the problem of being easily trapped into a local optimum while maintaining population diversity. The differential evolution algorithm is also used to make DGP-DE-K-mediods have strong global optimization capabilities. The DGP-DE-K-mediods clustering algorithm is designed and implemented in parallel under the Hadoop MapReduce framework and thus the time of the big data process has been significantly reduced. The programming model of MapReduce has been also described in detail for parallel cluster algorithm. A serial of simulation experiments are also done using UIC datasets and the intrusion detection processing of big data. Experimental results show that the overall detection effect of DGP-DE-K-mediods is significantly better than the existing intrusion detection algorithms.

Key words: cloud computing, parallel process, K-mediods clustering, differential evolution, intrusion detection system