Paper Title
Generating Positive & Negative Rules Using Efficient Apriori Algorithm
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 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. This algorithm work is accomplished in two phases: 1.first phase generate frequent and infrequent
pattern set.2.Efficiently generate positive and negative rules by using useful frequent pattern set. The process of rule
optimization we used genetic algorithm and for evaluate algorithm conducted the real world dataset such as heart disease
data and some standard data used from UCI machine learning repository.
Keywords- Positive & Negative Rules, Apriori Algorithm, Multi-scan Problem, MLMS Algorithm.