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Mining association rules is an essential task for knowledge discovery. From a large amount of data, potentially useful information may be discovered. Association rules are used to discover the relationships of items or attributes among huge data. These rules can be effective in uncovering unknown relationships, providing results that can be the basis of forecast and decision. The effective management of business is significantly dependent on the quality of its decision making. Past transaction data can be analyzed to discover customer behaviors such that the quality of business decision can be improved. The approach of mining association rules focuses on discovering large item sets, which are groups of items that appear together in an adequate number of transactions. The proposed method focuses on a combined approach to generate association rules from a large database of customer transactions. It also helps in identifying rarely occurring events. This approach scans the database once to construct an association graph and clustering tables and then traverses the graph to generate all large item sets.