  The Apriori algorithm that mines frequent itemsets is one of the most popular
and widely used data mining algorithms. Now days many algorithms have been
proposed on parallel and distributed platforms to enhance the performance of
Apriori algorithm. They differ from each other on the basis of load balancing
technique, memory system, data decomposition technique and data layout used to
implement them. The problems with most of the distributed framework are
overheads of managing distributed system and lack of high level parallel
programming language. Also with grid computing there is always potential
chances of node failures which cause multiple re-executions of tasks. These
problems can be overcome by the MapReduce framework introduced by Google.
MapReduce is an efficient, scalable and simplified programming model for large
scale distributed data processing on a large cluster of commodity computers and
also used in cloud computing. In this paper, we present the overview of
parallel Apriori algorithm implemented on MapReduce framework. They are
categorized on the basis of Map and Reduce functions used to implement them
e.g. 1-phase vs. k-phase, I/O of Mapper, Combiner and Reducer, using
functionality of Combiner inside Mapper etc. This survey discusses and analyzes
the various implementations of Apriori on MapReduce framework on the basis of
their distinguishing characteristics. Moreover, it also includes the advantages
and limitations of MapReduce framework.
