article · 2015

An improved Fuzzy C-Means algorithm based on MapReduce

Cited by 11Abstract and details only

Abstract

In order to solve the problem that the Fuzzy C-Means algorithm is sensitive to the initial clustering center, we use the Canopy algorithm to carry out the quick and rough clustering. At the same time, to avoid the blindness of the Canopy algorithm, we put forward an improved Canopy-FCM algorithm based on a max-min principle. In allusion to the problem that the FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved Canopy-FCM algorithm. Experimental results show: the improved Canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the Canopy-FCM and the FCM algorithm based on MapReduce, and the improved Canopy-FCM algorithm based on Hadoop has better speed-up ratio than the Canopy-FCM based on the Standalone mode.

Cite this paper

APA 7

Yu, Q., & Ding, Z. (2015). An improved Fuzzy C-Means algorithm based on MapReduce. https://doi.org/10.1109/bmei.2015.7401581

MLA 9

Yu, Qìng, and Zhimin Ding. “An improved Fuzzy C-Means algorithm based on MapReduce.” 2015, pp. 634–38, https://doi.org/10.1109/bmei.2015.7401581.

Chicago

Yu, Qìng, and Zhimin Ding. 2015. “An improved Fuzzy C-Means algorithm based on MapReduce.” https://doi.org/10.1109/bmei.2015.7401581.

Harvard

Yu, Q. and Ding, Z. (2015) ‘An improved Fuzzy C-Means algorithm based on MapReduce’. Available at: https://doi.org/10.1109/bmei.2015.7401581.

BibTeX

@article{yu2015improved,
  title = {{An improved Fuzzy C-Means algorithm based on MapReduce}},
  author = {Yu, Qìng and Ding, Zhimin},
  pages = {634--638},
  year = {2015},
  doi = {10.1109/bmei.2015.7401581}
}

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