ANG - a combination of Apriori and graph computing techniques for frequent itemsets mining

The Apriori algorithm is one of the most well-known and widely accepted methods for the association rule mining. In Apriori, it uses a prefix tree to represent k-itemsets, generates k-itemset candidates based on the frequent (k−1)-itemsets, and determines the frequent k-itemsets by traversing the prefix tree iteratively based on the transaction records. When k is small, the execution of Apriori is very efficient. However, the execution of Apriori could be very slow when k becomes large because of the deeper recursion depth to determine the frequent k-itemsets. From the perspective of graph computing, the transaction records can be converted to a graph G(V,E), where V is the set of vertices of G that represents the transaction records and E is the set of edges of G that represents the relations among transaction records. Each k-itemset in the transaction records will have a corresponding connected component in G. The number of vertices in the corresponding connected component is the support of the k-itemset. Since the time to find the corresponding connected component of a k-itemset in G is constant for any k, the graph computing method will be very efficient if the number of k-itemsets is relatively small. Based on Apriori and graph computing techniques, a hybrid method, called Apriori and Graph Computing (ANG), is proposed to compute the frequent itemsets. Initially, ANG uses Apriori to compute the frequent k-itemsets and then switches to the graph computing method when k becomes large (where the number of k-itemset candidates is relatively small). The experimental results show that ANG outperforms both Apriori and the graph computing method for all test cases

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

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