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

Zhang, R, Chen, W, Hsu, T, Yang, H and Chung, Y (2017) 'ANG - a combination of Apriori and graph computing techniques for frequent itemsets mining.' The Journal of Supercomputing. ISSN 1573-0484

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Abstract

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.

Item Type:	Article
Keywords:	Apriori, graph computing, frequent itemset mining, data mining
Divisions:	Bath School of Design
Identification Number:	https://doi.org/10.1007/s11227-017-2049-z
Date Deposited:	19 Apr 2017 14:44
Last Modified:	05 Jan 2022 16:07
URI / Page ID:	https://researchspace.bathspa.ac.uk/id/eprint/9490

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