Mining Association Rules in Cloud Computing Environments using Modified Apriori Algorithm

Authors

  • Avinash Sharma  Research Scholar, Bansal Group of Institutions, Bhopal, Madhya Pradesh, India
  • Dr. N. K. Tiwari  Director, Bansal Group of Institutions, Bhopal, Madhya Pradesh, India

Keywords:

Data Mining, Cloud Computing Association Rules

Abstract

An association rule mining helps in finding relation between the items or item sets in the given data. The performance of the algorithm was evaluated by testing it in the cloud (EC2) by increasing the number of nodes in the testing set up. The association rules are developed on the basis of the frequent item set generated from the data. The frequent item set were generated following the Apriori algorithm. As the input data and number of distinct items in the data set is large, lots of space and memory is required. Association rules are dependency rules which predict occurrence of an item based on occurrences of other items. Apriori is the best-known algorithm to mine association rules. The Apriori algorithm had a major problem of multiple scans through the entire data. It required a lot of space and time. The modification in our paper suggests that we do not scan the whole database to count the support for every attribute. This is possible by keeping the count of minimum support and then comparing it with the support of every attribute. The support of an attribute is counted only till the time it reaches the minimum support value. In this paper we use Modified Apriori algorithm to mine the data from the cloud using sector/sphere framework with association rules.

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Published

2018-02-28

Issue

Section

Research Articles

How to Cite

[1]
Avinash Sharma, Dr. N. K. Tiwari, " Mining Association Rules in Cloud Computing Environments using Modified Apriori Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.629-634, January-February-2018.