A Graph-Based Approach for Mining Closed Frequent Patterns

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed frequent patterns is a further work of mining association rules, which aims to find the set of necessary subsets of frequent patterns that could be representative of all frequent patterns. In this paper, we design a graph-based approach, considering the character of data, to mine the closed frequent patterns efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the clique concept, the new algorithm can find the closed frequent patterns efficiently. The simulation results show that the new algorithm outperforms the FP-Growth algorithm in the execution time and the space of storage.