Mining Frequent Itemsetset Using Assosiation Rule

Data mining represent the process of extraction interesting and previously unknown knowledge from data. In this thesis we address the important data mining problem of discovering association rules in single-table and multiple-table database and we also introduce a generalization of database concept of functional dependency: the purity dependencies, which can be viewed as a type of rules that are information-ally more significant than association rule. An association rule expresses the dependence of a set of attributes value pairs, also called items, upon another set of items.