Research on Vague soft clustering algorithm based on MapReduce
- 10.2991/iccia-17.2017.54How to use a DOI?
- Vague soft sets, clustering, MapReduce
Aiming at the problems that traditional clustering algorithm based on Vague soft sets is difficult to deal with massive data, a parallel Vague soft clustering algorithm based on MapReduce is proposed. The algorithm calculate the similarity measure between Vague soft sets based on Map function and Reduce function of the MapReduce programming framework, and Vague similarity matrix is established as the basis for clustering at the same time. Secondly, the Vague matrix is partitioned according to the similarity matrix based on the idea of matrix partition, and we process the block matrix and merge the results based on MapReduce. Finally, the data items is clustered based on the similarity threshold. The contrast experiment between traditional Vague soft clustering algorithm and the new algorithm shows that the proposed algorithm has higher accuracy, it can not only achieve better speedup in large-scale data calculation but also divide project resources effectively and accurately.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Wei Wang AU - Junsheng Wu AU - Zhixiang Zhu PY - 2016/07 DA - 2016/07 TI - Research on Vague soft clustering algorithm based on MapReduce BT - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 317 EP - 326 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.54 DO - 10.2991/iccia-17.2017.54 ID - Wang2016/07 ER -