Enhancing Map-Reduce Framework for Bigdata with Hierarchical Clustering

MapReduce is a software framework that allows certain kinds of parallelizable or distributable problems involving large data sets to be solved using computing clusters. This paper introduces our experience of grouping internet users by mining a huge volume of web access log of up to 500 gigabytes. The application is realized using hierarchical clustering algorithms with Map-Reduce, a parallel processing framework over clusters. However, the immediate implementation of the algorithms suffers from efficiency problem for both inadequate memory and higher execution time. This paper presents an efficient hierarchical clustering method of mining large datasets with Map-Reduce. The method includes two optimization techniques: Batch Updating to reduce the computational time and communication costs among cluster nodes, and Co-occurrence based feature selection to decrease the dimension of feature vectors and eliminate noise features. KEYWORDS-Hierarchical clustering, Batch Updating, Feature selection, MapReduce, Bigdata