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International Conference on Advanced Data Mining and Applications

ADMA 2011: Advanced Data Mining and Applications pp 29–41Cite as

A False Negative Maximal Frequent Itemset Mining Algorithm over Stream

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7120))

Abstract

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper focuses on mining maximal frequent itemsets approximately over a stream landmark model. We separate the continuously arriving transactions into sections and maintain them with 3-tuple lists indexed by an extended direct update tree; thus, an efficient algorithm named FNMFIMoDS is proposed. In our algorithm, we employ the Chernoff Bound to perform the maximal frequent itemset mining in a false negative manner; plus, we classify the itemsets into categories and prune some redundant itemsets, which can further reduce the memory cost, as well guarantee our algorithm conducting with an incremental fashion. Our experimental results on two synthetic datasets and two real world datasets show that with a high precision, FNMFIMoDS achieves a faster speed and a much reduced memory cost in comparison with the state-of-the-art algorithm.

This research is supported by the National Science Foundation of China(61100112), and Program for Innovation Research in Central University of Finance and Economics.

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Author information

Authors and Affiliations

  1. School of Information, Central University of Finance and Economics, Beijing, China, 100081

    Haifeng Li & Ning Zhang

Authors
  1. Haifeng Li
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  2. Ning Zhang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China

    Jie Tang  & Jianyong Wang  & 

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China

    Irwin King

  3. Faculty of Engineering and Information Technology, University of Technology, 2007, Sydney, NSW, Australia

    Ling Chen

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© 2011 Springer-Verlag Berlin Heidelberg

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Li, H., Zhang, N. (2011). A False Negative Maximal Frequent Itemset Mining Algorithm over Stream. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-25853-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25852-7

  • Online ISBN: 978-3-642-25853-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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