Parallel clustering of very large document datasets with MapReduce

To develop fast and efficient methods to cluster mass document data is one of the hot issues of current data mining research and applications. In order to ensure the clustering result and simultaneously improve the clustering efficiency, a document clustering algorithm was proposed based on searching a document pair with minimum similarity for each other and its distributed parallel computing models were provided. Firstly a document similarity measure was presented using a vector space model (VSM); then bisecting clustering was raised combining the bisecting K-means and the proposed initial cluster center selection approach to find the optimized cluster centroids by once partitioning; finally a distributed parallel document clustering model was designed for cloud computing based on MapReduce framework. Experiments on Hadoop platform, using real document datasets, showed the obvious efficiency advantages of the novel document clustering algorithm compared to the original bisecting K-means with an equivalent clustering result, and the scalability of parallel clustering with different data sizes and different computation node numbers was also evaluated.