FP-Growth Based New Normalization Technique for Subgraph Ranking
The various problems in large volume of data area have been solved using frequent itemset discovery algorithms. As data mining techniques are being introduced and widely applied to non-traditional itemsets, existing approaches for finding frequent itemsets were out of date as they cannot satisfy the requirement of these domains. Hence, an alternate method of modeling the objects in the said dataset, is graph. Modeling objects using graphs allows one 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. In this paper, the authors present an efficient algorithm for ranking of such frequent subgraphs.