An efficient algorithm for mining erasable itemsets

Authors:
Zhihong Deng;Xiaoran Xu
Affiliations:
Key Laboratory of Machine Perception, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing, China and The State Key Lab of Computer Science, In ...;The State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. Based on PID_list, we propose a new algorithm called VME for mining erasable itemsets efficiently. The main advantage of VME algorithm is that the gain of an itemset can be computed efficiently via union operations on product id_nums. In addition, VME algorithm can also automatically prune irrelevant data. For evaluating VME algorithm, we have conducted experiments on six synthetic product databases. Our performance study shows that the VME algorithm is efficient and is on average over two orders of magnitude faster than the META algorithm, which is the first algorithm for dealing with the problem of erasable itemsets mining.