Map / Reduce Deisgn and Implementation of Apriori Alogirthm for handling voluminous data-sets

TL;DR

This paper presents a MapReduce-based design and implementation of the Apriori algorithm to efficiently analyze large voluminous structured datasets using distributed computing frameworks like Hadoop.

Contribution

It introduces a novel MapReduce approach for Apriori, enabling scalable frequent itemset mining on big data in distributed environments.

Findings

Efficient processing of large datasets using Hadoop MapReduce.

Improved scalability of Apriori algorithm in distributed systems.

Demonstrated effectiveness on voluminous structured data.

Abstract

Apriori is one of the key algorithms to generate frequent itemsets. Analyzing frequent itemset is a crucial step in analysing structured data and in finding association relationship between items. This stands as an elementary foundation to supervised learning, which encompasses classifier and feature extraction methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the structured data in scientific domain are voluminous. Processing such kind of data requires state of the art computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of the cluster frameworks in distributed environment that helps by distributing voluminous data across a number of nodes in the framework. This paper focuses on map/reduce design and implementation of Apriori algorithm for structured data analysis.