Review of several improved Apriori algorithms on Hadoop-MapReduce environment

A.L.SAYETH, SAABITH and AZURALIZA, ABU BAKAR and ELANKOVAN, SUNDARARAJAN (2015) Review of several improved Apriori algorithms on Hadoop-MapReduce environment. In: Third International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2015, 11-12 APRIL 2015, Kuala Lumpur, Malaysia.

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Association Rule Mining (ARM) plays a significant role in the data mining techniques. ARM aims to reveal association relationship among different items in large datasets. The Apriori algorithm is one of the most broadly used algorithm in ARM that collects the item sets which frequently occur in order to discover association rule in massive datasets. The original Apriori algorithm is for the sequential (single node or computer) environment. This Apriori algorithm has many drawbacks to process huge datasets. Many researches have been carried out for parallelizing the Apriori algorithm. This study does a survey on few good improved and revised approaches of parallel Apriori algorithm on Hadoop- MapReduce environment. Hadoop-MapReduce framework is a programming model that efficiently and effectively processing enormous databases in parallel on large clusters of commodity hardware in a reliable, and fault- tolerant manner. This survey will provide an overall view of parallel Apriori algorithm implementation over Hadoop-MapReduce environment and briefly discussing Hadoop challenges and advantages.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: ARM, Apriori, Hadoop, MapReduce.
Depositing User: Mr. John Steve
Date Deposited: 07 May 2019 10:09
Last Modified: 07 May 2019 10:09

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