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Association rule mining is a time consuming process due to involving bothdata intensive and computation intensive nature. In order to mine large volumeof data and to enhance the scalability and performance of existing sequentialassociation rule mining algorithms, parallel and distributed algorithms aredeveloped. These traditional parallel and distributed algorithms are based onhomogeneous platform and are not lucrative for heterogeneous platform such asgrid and cloud. This requires design of new algorithms which address the issuesof good data set partition and distribution, load balancing strategy,optimization of communication and synchronization technique among processors insuch heterogeneous system. Grid and cloud are the emerging platform fordistributed data processing and various association rule mining algorithms havebeen proposed on such platforms. This survey article integrates the briefarchitectural aspect of distributed system, various recent approaches of gridbased and cloud based association rule mining algorithms with comparativeperception. We differentiate between approaches of association rule miningalgorithms developed on these architectures on the basis of data locality,programming paradigm, fault tolerance, communication cost, partition anddistribution of data sets. Although it is not complete in order to cover allalgorithms, yet it can be very useful for the new researchers working in thedirection of distributed association rule mining algorithms.

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