EARMGA and Apriori Algorithm’s Performance Evaluation for Association Rule Mining
Sandeep Pratap Singh1, Dharani Kumar Talapula2
1Sandeep Pratap Singh, Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
2Dharani Kumar Talapula, Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6982-6987 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2144109119/2019©BEIESP | DOI: 10.35940/ijeat.A2144.109119
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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: Association rule mining techniques are important part of data mining to derive relationship between attributes of large databases. Association related rule mining have evolved huge interest among researchers as many challenging problems can be solved using them. Numerous algorithms have been discovered for deriving association rules effectively. It has been evaluated that not all algorithms can give similar results in all scenarios, so decoding these merits becomes important. In this paper two association rule mining algorithms were analyzed, one is popular Apriori algorithm and the other is EARMGA (Evolutionary Association Rules Mining with Genetic Algorithm). Comparison of these two algorithms were experimentally performed based on different datasets and different parameters like Number of rules generated, Average support, Average Confidence, Covered records were detailed.
Keywords: Data mining, Apriori algorithm, EARMGA, Association rule mining.