Volume 6 Number 11 (Nov. 2011)
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JSW 2011 Vol.6(11): 2121-2128 ISSN: 1796-217X
doi: 10.4304/jsw.6.11.2121-2128

An Efficient Mining Algorithm by Bit Vector Table for Frequent Closed Itemsets

Keming Tang1, 2, 3, Caiyan Dai2, Ling Chen2, 4
1College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2Department of Computer Science, Yangzhou University, Yangzhou, China
3Department of Software Engineering, Yancheng Teachers University, Yancheng, China
4State Key Lab of Novel Software Technology, Nanjing University, Nanjing, China


Abstract—Mining frequent closed itemsets in data streams is an important task in stream data mining. In this paper, an efficient mining algorithm (denoted as EMAFCI) for frequent closed itemsets in data stream is proposed. The algorithm is based on the sliding window model, and uses a Bit Vector Table (denoted as BVTable) where the transactions and itemsets are recorded by the column and row vectors respectively. The algorithm first builds the BVTable for the first sliding window. Frequent closed itemsets can be detected by pair-test operations on the binary numbers in the table. After building the first BVTable, the algorithm updates the BVTable for each sliding window. The frequent closed itemsets in the sliding window can be identified from the BVTable. Algorithms are also proposed to modify BVTable when adding and deleting a transaction. The experimental results on synthetic and real data sets indicate that the proposed algorithm needs less CPU time and memory than other similar methods.

Index Terms—data mining, frequent closed itemsets, bit vector table, data stream, sliding window

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Cite: Keming Tang, Caiyan Dai, Ling Chen, "An Efficient Mining Algorithm by Bit Vector Table for Frequent Closed Itemsets," Journal of Software vol. 6, no. 11, pp. 2121-2128, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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