An improved Apriori-based algorithm for association rules mining
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Efficient mining of frequent itemsets in social network data based on MapReduce framework
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Parallel system is mainly composed of parallel algorithms which are cost optimal. In this paper a parallel algorithm the Hash Partitioned Apriori (HPA) is taken into consideration. HPA partitions the candidate itemsets among processors using a hash function, like the hash join in relational databases. HPA effectively utilizes the whole memory space of all the processors, hence it works well for large scale data mining in a parallel and distributed environment. The optimization technique of dynamic data allocation is discussed for the execution of this application. This technique is applied in a parallel and distributed environment. Writing parallel data mining algorithms in a distributed environment is a non-trivial task. The main purpose of the proposed method is to meet certain challenges associated with parallel and distributed data mining such as i) minimizing I/O ii) Increasing processing speed iii) Communication cost.