Research on Algorithm for Mining Frequent Itemsets Based on Cloud Computing

Abstract

This thesis aims to discuss the problems now existing in mining probabilistic of MapReduce Apriori, and to put forward an algorithm about mining probabilistic frequent itemsets based on cloud computing. This algorithm proposes to reduce the quantity of candidate item sets by the strategy of designing to decrease candidate item sets, and divide data sets and candidate item sets into related nodes in order to minimize candidate item sets. By compressing the transaction set to accomplish connecting optimization, it can avoid producing massive alternatives item sets in the process of self-link to improve the operation efficiency of algorithm.