Combined Mining Approach and Pattern Discovery in “Online Shopping Application”

Now a day it’s mandatory to use data mining tools and services. Business application data involve data from large heterogeneous data sources as well as data such as user preferences and business impacts. Generally the expectation of business person is to view single view of business settings, analytical reports from complex data that help business users to make decisions.Traditional data mining systems has so many drawbacks in order to overcome the drawbacks; a novel idea is combined mining. Combined mining is effective technique for extracting and constructing actionable complex knowledge, patterns .The most common approaches in data mining have been extended to the multi-relational, multi-feature, multi- source and multi-method combined mining.This paper illustrates the concept of combined association rule to find actionable knowledge from that association rule. A traditional association mining often produces large numbers of association rules and it is difficult for users to understand such rule. Combined mining is a post processing method for association rules generated. In this approach, first the association rule are filtered by varying support and confidence levels ,then using the interestingness measure rules, association rules are further extracted.This paper briefly illustrates the concept of combined mining, general frameworks, paradigms and basic processes for combined mining.