A General Survey on Multidimensional And Quantitative Association Rule Mining Algorithms

Data mining is one of the significant topics of research in recent years. Association rule is a method for discovering interesting relations between variables in large databases. Support and Confidence are the two basic parameters used to study the threshold values for each database. In this paper, an overall survey of the algorithms implementing the multidimensional and quantitative data is presented. It also illustrates the various approaches in association rule. The concept of Bit Mask Search Algorithm can be identified and suggested to effectively generate the frequent Item sets from large database. Moreover, the algorithm is best suited for multidimensional and quantitative datasets. It describes the essential role of multidimensional and Quantitative data in Association rule