Parallel particle swarm optimization clustering algorithm based on MapReduce methodology

@article{Aljarah2012ParallelPS,
  title={Parallel particle swarm optimization clustering algorithm based on MapReduce methodology},
  author={Ibrahim Aljarah and Simone A. Ludwig},
  journal={2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)},
  year={2012},
  pages={104-111}
}
Large scale data sets are difficult to manage. Difficulties include capture, storage, search, analysis, and visualization of large data. In particular, clustering of large scale data has received considerable attention in the last few years and many application areas such as bioinformatics and social networking are in urgent need of scalable approaches. The new techniques need to make use of parallel computing concepts in order to be able to scale with increasing data set sizes. In this paper… CONTINUE READING
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Parallel kmeans clustering based on mapreduce

  • Z. Weizhong, M. Huifang, H. Qing
  • Proceedings of the CloudCom ’09. Berlin…
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