Abstract: Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the studies adopt an Apriori-like candidate set generation-and-test approach. However, the candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. Frequent-pattern tree (FP-tree) structure, which is an extended prefix -tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Training dataset repeatedly produces massive amount of rules. It’s very tough to store, retrieve, prune, and sort a huge number of rules proficiently before applying to a classifier. In such situation FP is the best choice but problem with this approach is that it generates redundant FP Tree. In this paper, the limitation of these two methods and an integrated techniques of both Apriori and FP-Growth, is used to overcome the limitation of existing methods.