Review paper on finding Association rule using Apriori Algorithm in Data mining for finding frequent pattern
Kinjal Jain, Bhairavi Raut · 2015
Because of the rapid growth in worldwide information, efficiency of association rules mining (ARM) has been concerned for several years. Association rule mining plays vital part in knowledge mining. The difficult task is discovering knowledge or useful rules from the large number of rules generated for reduced support In this paper, based on the Apriori algorithm association rules is based on interestingness measures such as support, confidence and so on. Confidence value is a measure of rule's strength, while support value corresponds to statistical significance. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall System. In this algorithm, we will create association rules depending upon the dataset available in the database. The algorithm majorly works on finding the minimal confidence and so association rules which frequently used and follow the minimum confidence. So the research part of this paper is this by changing the value of minimum confidence, gives different association rules. The value of minimum confidence is high then rules filtered more accurately..