Improved extraction of quantitative rules using Best M Positive Negative Association Rules Algorithm
Sheetal Naredi, Rushali Deshmukh · 2015
Mining association rules is a fundamental data mining task. Association rule greatly help to identify trends and pattern from huge data set. Algorithms for mining association rules put more stress on positive rules rather than negative rules. Negative rules specify the attribute present in the data set to the attribute absent. In this paper we propose an algorithm BMPNAR, Best M Positive Negative Association Rules Algorithm, in order to get a reduced set of limited number of association rules which are then classified using Firefly algorithm. The algorithm BMPNAR is an extension to MOPNAR algorithm. It is a combination of MOPNAR and Topk algorithm. We let the user specify the number of rules to be generated. It gives us ranked association rules. These rules are then classified by applying FireFly algorithm for analysis purpose. The system designed is supposed to generate best M classified rules. The dataset used is Keel Dataset. We give the comparative study of previous and new algorithm in terms of execution time and space required.