Mining of High Average-Utility Pattern Using Multiple Minimum Thresholds in Big Data

Abstract

In the past years most of the research have been conducted on high average-utility itemset mining (HAUIM) with wide applications. However, most of the methods are used for centralized databases with a single machine performing the mining job. Existing algorithms cannot be applied for big data. We try to solve this issue, by developing a new method for mining high average-utility itemset mining in big data. Map Reduce also used in this paper. Many algorithms were proposed only mine HAUIs using a single minimum high average-utility threshold. In this paper we also try solve this by mining HAUIs multiple minimum high average-utility thresholds. We have developed two pruning methods namely Reduction of utility co-occurrence pruning Method (RUCPM) and Pruning without Scanning Database (PWSD).