Abstract
Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the mining of positive and negative rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. A number of algorithms are wonder performance but produce large number of negative association rule and also suffered from multi-scan problem. The idea of this paper is to eliminate these problems and reduce large number of negative rules. Hence we proposed an improved approach to mine interesting positive and negative rules based on genetic and MLMS algorithm. In this method we used a multi-level multiple support of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of MLMS and genetic algorithm. This paper proposed a new algorithm (MIPNAR_GA) for mining interesting positive and negative rule from frequent and infrequent pattern sets. The algorithm is accomplished in to three phases: a).Extract frequent and infrequent pattern sets by using apriori method b).Efficiently generate positive and negative rule. c).Prune redundant rule by applying interesting measures. The process of rule optimization is performed by genetic algorithm and for evaluation of algorithm conducted the real world dataset such as heart disease data and some standard data used from UCI machine learning repository.
Highlights
Association rule mining is a method to identify the hidden facts in large instances database and draw interferences on how subsets of items influence the existence of other subsets
Generalized Negative Association Rules (GNAR) is produced interesting negative rules,this approach could speed up execution time efficiently through the domain taxonomy tree and extract interesting rules advantage of taxonomy tree is to eliminate large number of useless transaction [9]
This paper proposed a novel algorithm for optimization of association rule mining, the proposed algorithm resolves the problem of negative rule generation and optimized the process of rule generation
Summary
Association rule mining is a method to identify the hidden facts in large instances database and draw interferences on how subsets of items influence the existence of other subsets. All generalized frequent pattern sets are not very efficient because a segment of the frequent pattern sets are redundant in the association rule mining This is why, traditional mining algorithm produces some uninteresting rules or redundant rules along with the interesting rule. This problem can be overcome with the help of genetic algorithm. Genetic algorithm is produced by optimized result as compare to the greedy algorithm because it performs a comprehensive search and better attributes interaction [1]. Negative association rule mining is adopted where a domain has too many factors and large number of infrequent pattern sets in transaction database. The process of optimization of interesting association rule mining used genetic algorithm.
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