Pa wi parallelweighted itemset mining by means of mapreduce

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2020 – 2021
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PaWI: ParallelWeighted Itemset Mining by means of MapReduce
Abstract :
Frequent pattern mining is the most important phase of association rule mining
process because of its time and space complexity. Several methods have
attempted to improve the performance of association rule mining by enhancing
frequent pattern mining efficiency. Due to the large size of the data-sets and huge
amounts of data which should be mined, many parallel and distributed mining
approaches have been introduced to divide data-sets or to distribute mining
processes between multiple processors or computers and thus, improve the
efficiency of the mining process. In this paper, we propose a hadoop-based parallel
implementation of PrePost+ algorithm for frequent itemset mining. In our parallel
approach, the process of constructing N-Lists of itemsets has been distributed
between the mappers and the operation of the final pruning process and extracting
frequent itemsets has been carried out by reducers in a map-reduce parallel
programming model. The experimental results show that our hadoop-based
PrePost+(HBPrePost+) algorithm outperforms one of the best existing parallel
methods of frequent itemset mining (PARMA) in terms of execution time

The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper. To check the varying point of context change in streaming transaction we have developed a window structure which will be in two levels and supports in fixing the window size instantly and controls the heterogeneities and assures homogeneities among transactions added to the window. To minimize the memory utilization, computational cost and improve the process scalability, this design will allow fixing the coverage or support at window level. Here in this document, an incremental mining of frequent item-sets from the window and a context variation analysis approach are being introduced. The complete technology that we are presenting in this document is named as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWSCVA). There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this design we have used window size change to represent the conceptual drift in an information stream. As it were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The experiments that we have executed and documented proved that the algorithm that we have designed is much efficient than that of existing.

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