  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.
