Performance Improvement of Classifier Using Attribute Selection with Association Rule Mining Technique


Authors : Rutuja Shinde , Saee Joshi , Aishwarya Gunjal , Dr. K. Rajeswar

Volume/Issue : Volume 3 - 2018, Issue 1 - January

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/b6ArpF

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Data mining refers to extracting knowledge from huge amount of data. Apriori algorithm is the paradigmatic algorithm of association rule, which enumerates all of the frequent item sets. When this algorithm encounters dense but noisy data, large number of extensible patterns emerge and hence, the algorithm’s performance diminishes dramatically. In order to find more worth rules. this paper explains the radicals of Association Rule Mining (ARM)and moreover acquire a general framework. This paper proposes selection of best attributes from the best rules obtained with Apriori algorithm. After that truly explore their influences and carry forward several run time demonstrations using a classification technique Iterative Dichotomiser (ID3). The research describes algorithmic discussion and comparison of the performance of classification algorithm with and without best attributes.

Keywords : Data Mining, Association Rules, ID3, Apriori Algorithm, Frequent item, Performance.

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