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mining applications involve complex data like multiple heterogeneous data sources, different user preference and create decision making activities. The comprehensive, useful information may not be obtained by using the single data mining method in the form of informative patterns as that would consume more time and space. Combined mining is a hybrid mining approach for mining informative patterns from single or multiple data sources, many features abstraction and merging multiple methods as per the requirements. Some concepts show hybrid or combined mining approach. In this paper, multi method combined mining methodology is designed. Proposed method combine apriori algorithm with Multi-Objective Evolutionary Algorithm. It helps to improve searching for the exact products from complex data. Proposed method combine apriori algorithm with multi-objective evolutionary algorithm to improve searching of documents from complex data. It addressed challenging problems in combined mining and summarized and proposed effective pattern merging and interaction paradigms, combined pattern types, such as pair patterns and cluster patterns, interestingness measures. In the proposed method, by using apriori algorithm, calculate the support and confidence of the frequent item set which improve results of searching by a using multi-objective evolutionary algorithm. Keywordsmining approach, Multi-objective Evolutionary algorithm, Apriori algorithm, Knowledge Discovery combined mining, complex data, data mining, multiple source data mining.

Discovering in Formative Knowledge using Combined Mining Approach