Hash Partitioned apriori in Parallel and Distributed Data Mining Environment with Dynamic Data Allocation Approach

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.