A Survey On Association Rule Mining

Data mining is a process of extraction of valuable and unknown information from the large databases from different perspectives and summarizing it into useful information. The information can be converted into knowledge about historical patterns. Many Algorithms have been proposed to mine association rule that uses support and confidence as a constraint. Association rules generated from mining data at multiple levels of Abstraction are called multiplelevel or multilevel association rules. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework. Apriori finds its application in areas of data mining, finding association between attributes and in prediction systems. A corelation factor (threshold) is also used several times to increase the efficiency of Apriori algorithm.