Efficient mining of multi dimensional data using combined patterns

In data mining, Mining Informative Knowledge from heterogeneous data has been recognized as one of the most challenging problem. The proposed concept of combined mining used to extract actionable knowledge from complex data. The proposed concept is logically divided into three functional requirements namely multi source combined mining, multi feature combined mining and multi method combined mining. It combines the patterns either from the multiple data sources or from the multiple features data. Association-Classification methods are used in the multi mining method to mine the efficient patterns from the heterogeneous data. The Association algorithm namely Apriori algorithm is used to mine the frequent patterns from the data sources. After finding the frequent patterns from this method, again another method called classification algorithm namely ID3 is used to classify the efficient combined patterns from the heterogeneous data so that it will be easy for decision makers to take correct decisions for business impact. A bank application is taken as example and identified the efficient combined patterns for the clearance of the loan and improving the service objectives of the bank.