Open AccessMining of High Average-Utility Pattern Using Multiple Minimum Thresholds in Big Data
Author(s) -
R. Vasumathi,
Suriya Murugan
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.s2.2024
Subject(s) - pruning , computer science , data mining , big data , reduction (mathematics) , machine learning , artificial intelligence , mathematics , geometry , agronomy , biology
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).