A Comparative Study of Association Rule Mining Algorithms on Grid and Cloud Platform

TL;DR

This paper compares association rule mining algorithms implemented on grid and cloud platforms, highlighting their architectural differences, challenges, and performance considerations for large-scale data processing.

Contribution

It provides a comparative analysis of recent grid and cloud-based association rule mining algorithms, focusing on data partitioning, load balancing, and communication strategies.

Findings

Differentiates algorithms based on data locality and fault tolerance.

Highlights challenges in heterogeneous platform data distribution.

Provides insights for designing efficient distributed association rule mining algorithms.

Abstract

Association rule mining is a time consuming process due to involving both data intensive and computation intensive nature. In order to mine large volume of data and to enhance the scalability and performance of existing sequential association rule mining algorithms, parallel and distributed algorithms are developed. These traditional parallel and distributed algorithms are based on homogeneous platform and are not lucrative for heterogeneous platform such as grid and cloud. This requires design of new algorithms which address the issues of good data set partition and distribution, load balancing strategy, optimization of communication and synchronization technique among processors in such heterogeneous system. Grid and cloud are the emerging platform for distributed data processing and various association rule mining algorithms have been proposed on such platforms. This survey article integrates the brief architectural aspect of distributed system, various recent approaches of grid based and cloud based association rule mining algorithms with comparative perception. We differentiate between approaches of association rule mining algorithms developed on these architectures on the basis of data locality, programming paradigm, fault tolerance, communication cost, partition and distribution of data sets. Although it is not complete in order to cover all algorithms, yet it can be very useful for the new researchers working in the direction of distributed association rule mining algorithms.