BibTeX:

@article{IJIRSTV2I5045,
     title={Improving the Performance Of Ms-Apriori Algorithm Using Dynamic Matrix Technique And Map-Reduce Framework},
     author={Rachna Chaudhary, Sachin Sharma and Vijay Kumar Sharma},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={5},
     pages={143--162},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I5045.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Data Mining refers to the process of mining useful data over large datasets. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. This is the reason that the research in data mining is carried out largely for business decision making rather than for academic importance. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Most of the algorithms need a specification of minimum support value as user input. Specifying minimum support values of items is not recommended as it leads to very less or very large rules. With a sufficiently high support value, the less frequent elements gets eliminated, leaving only the elements which are most frequent. Thus, knives and spoons may get eliminated leaving only biscuits and milk. One approach for this problem is proposed by MsApriori Algorithm. However, both Apriori and MsApriori are computationally complex and need large computational time for large datasets over traditional machines. One solution to this problem is proposed by Dynamic Matrix Apriori which is much faster as compared to traditional Apriori in the generation of candidate sets. The contribution of this paper is twofold. It first proposed a method to use MsAprioiri using Dynamix Matrix Technique. It then proposes a framework to use the Algorithm under the Map Reduce Programming model. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.

Apriori Algorithm, Association rule mining, Multiple Item Support, MapReduce