Graph Based New Approach for Frequent Pattern Mining

Association rule mining is a function of data mining research domain and frequent pattern mining is an essential part of it. Most of the previous studies on mining frequent patterns based on an Apriori approach, which required more number of database scans and operations for counting pattern supports in the database. Since the size of each set of transaction may be massive that it makes difficult to perform traditional data mining tasks. This research intends to propose a graph structure that captures only those itemsets that needs to define a sufficiently immense dataset into a submatrix representing important weights and does not give any chance to outliers. We have devised a strategy that covers significant facts of data by drilling down the large data into a succinct form of an Adjacency Matrix at different stages of mining process. The graph structure is so designed that it can be easily maintained and the trade off in compressing the large data values is reduced. Experimental results show the effectiveness of our graph based approach.