A Frequent Subgraph Discovery Algorithm Based on Apriori’s Idea
Sun Xiao-lin · Computer Engineering and Science · 2007
Nowadays association rule mining techniques are applied to many non-traditional domains, existing approaches for frequent itemsets discovery are inapplicable. An alternate way to solve these problems is to represent the transactions of those domains by graph,and find the frequent subgraphs by using graph-based data mining techniques. We propose a new algorithm named SLAGM, which is based on Apriori’s idea. It can mine frequent subgraphs from simple graphs efficiently. After being evaluated by experiments with synthetic datasets, this algorithm show better performance than another subgraph mining algorithm AGM.