MapReduce Based Method for Big Data Semantic Clustering
Jie Yang, Xiaoping Li · 2013
Big data analysis is very hot in cloud computing environments. How to automatically map heterogeneous data with the same semantics is one of the key problems in big data analysis. A big data clustering method based on the MapReduce framework is proposed in this paper. Big data are decomposed into many data chunks for parallel clustering, which is implemented by Ant Colony. Data elements are moved and clustered by ants according to the presented criterion. The proposed method is compared with the MapReduce framework based k-means clustering algorithm on a great amount of practical data. Experimental results show that the proposal is much effective for big data clustering.