An Enhancement of Association Rule Mining Algorithm
Gurpreet Batra1, Alpana Vijay Rajoriya2
1Er. Gurpreet Batra, M. Tech Department of (CSE), Lovely Professional University, Phagwara, Punjab, India.
2Ms. Alpana Vijay Rajoriya, Asst. Prof. Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.
Manuscript received on November 21, 2014. | Revised Manuscript received on December 07, 2014. | Manuscript published on December 30, 2014. | PP: 115-117 | Volume-4 Issue-2, December 2014. | Retrieval Number: B3627124214/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the well-researched and most important techniques of mining data is Association Rule Mining. Association Rules as the name itself indicates includes finding correlations among sets of items in transaction database. Most famous algorithm of association rule mining is Apriori is used for knowledge discovery. The proposed work is based on finding association rules considering the multidimensionality of the attributes and reducing the computation time that will increase the efficiency. Proposed work will improve the existing Apriori algorithm and will reduce some of the drawbacks of the existing algorithm.
Keywords: Association rules, Confidence, Support count, Apriori Algorithm.