We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that is fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of mag-nitude for large problems. We also show how the best frequent pattern mining has become one of the most popular data mining approaches for the analysis of purchasing patterns. There are techniques such as Apriori and FP-Growth, which were typically restricted to a single concept level. We extend our research to discover cross – level frequent patterns in multi-level environments. Unfortunately, little research has been paid to this research area. Mining cross – level frequent pattern may lead to the discovery of mining patterns at different levels of hierarchy. In this study a transaction reduction technique with FP-tree based bottom up approach is used for mining cross-level pattern. This method is using the concept of reduced support.