Frequent pattern mining is an essential method of data mining that is used to extract interesting patterns from massive databases. Traditional methods use single minimum support threshold to find out the complete set of frequent patterns. However, in real word applications, using single minimum support threshold is not adequate since it does not reflect the nature of each item and causes a problem called rare item problem. Recently, several methods have been studied to tackle this problem by avoiding using single minimum item support threshold. The nature of each item is considered where different items are specified with different minimum support thresholds. By this, the complete set of frequent patters are generated without creating uninteresting patterns and losing substantial patterns. In this thesis, we propose an efficient method, Multiple Item Support Frequent Pattern growth algorithm, MISFP-growth, to mine the complete set of frequent patterns with multiple item support thresholds. In this method, Multiple Item Support Frequent Pattern tree, MISFP-Tree, is constructed to store all crucial information to mine frequent patterns. Since in the construction of the MISFP-Tree is done with respect to minimum of Multiple Itemset Support values; pruning and reconstruction phases are not required. To show the efficiency of the proposed method, it is compared with a recent tree-based algorithm, CFP-growth++. To evaluate the performance of the proposed algorithm, various experiments are conducted on both real and synthetic datasets. Experimental results reveal that MISFP-growth outperforms the previous algorithm in terms of execution time, memory space as well as scalability.