Highly quantitative mining association rules with clustering

Data mining is a step in the knowledge discovery process consisting of certain data mining algorithms that, under some acceptable computational efficiency limitations, finds patterns or models in data. Association rule analysis starts with transactions containing one or more products or service offerings and some rudimentary information about the transaction. This paper describes the clustering in association rules using quantitative attributes, which are expressive multi-dimensional generalized association rules for university admission. University database which is vast and which has interrelated item sets is chosen for mining. Quantitative attributes can have a very wide range of values defining their domain. 2-Dimensional quantitative association rules predicting the condition on the right hand side, given the quantitative attributes age, status and citizen. In this method age is the main criteria for quantitative process. The Strong association rules are obtained by this prescribed method.