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Frequent-Itemset Mining Using Locality-Sensitive Hashing

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Computing and Combinatorics (COCOON 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9797))

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Abstract

The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LSH defined over Hamming distance and Jaccard similarity.

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Notes

  1. 1.

    Note that computing support is an I/O intensive operation and involves reading every transaction.

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Author information

Authors and Affiliations

  1. Indraprastha Institute of Information Technology-Delhi (IIIT-D), New Delhi, India

    Debajyoti Bera

  2. TCS Innovation Labs, New Delhi, India

    Rameshwar Pratap

Authors
  1. Debajyoti Bera
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  2. Rameshwar Pratap
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Corresponding author

Correspondence to Debajyoti Bera .

Editor information

Editors and Affiliations

  1. Virginia Commonwealth Univ , Richmond, Virginia, USA

    Thang N. Dinh

  2. University of Florida , Gainesville, Alabama, USA

    My T. Thai

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Bera, D., Pratap, R. (2016). Frequent-Itemset Mining Using Locality-Sensitive Hashing. In: Dinh, T., Thai, M. (eds) Computing and Combinatorics . COCOON 2016. Lecture Notes in Computer Science(), vol 9797. Springer, Cham. https://doi.org/10.1007/978-3-319-42634-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-42634-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42633-4

  • Online ISBN: 978-3-319-42634-1

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