New Normalized Technique using FP Growth for Subgraph Ranking

Abstract – Data mining techniques are being introduced and widely applied to non-traditional itemsets; existing approaches for finding frequent item sets were out of date as they cannot satisfy the requirement of these domains. Hence, an alternate method of modeling the objects in the said data set is graph. Modeling objects using graphs allows us to represent an arbitrary relation among entities. The graph is used to model the database objects. Within that model, the problem of finding frequent patterns becomes that of finding subgraphs that occur frequently over the entire set of graphs. It presents an efficient algorithm for ranking of such frequent subgraphs. This proposed ranking method is applied to the FP-growth method for discovering frequent subgraphs.