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Efficient Single Pass Ordered Incremental Pattern Mining

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Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 7790))

Abstract

Since the introduction of FP-growth using FP-tree there has been a lot of research into extending its usage to data stream or incremental mining. Most incremental mining adapts the Apriori algorithm. However, we believe that using a tree based approach would increase performance as compared to the candidate generation and testing mechanism used in Apriori. Despite this, FP-tree still requires two scans through a dataset. In this paper we present a novel tree structure called Single Pass Ordered Tree, SPO-Tree, that captures information with a single scan for incremental mining. All items in a transaction are inserted/sorted based on their frequency. The tree is reorganized dynamically when necessary. SPO-Tree allows for easy maintenance in an incremental or data stream environment.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Auckland, New Zealand

    Yun Sing Koh & Gillian Dobbie

Authors
  1. Yun Sing Koh
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  2. Gillian Dobbie
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Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University,, 118 route de Narbonne, 31062, Toulouse Cedex, France

    Abdelkader Hameurlain

  2. Institute for Application Oriented Knowledge Processing, 4020, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Roland Wagner

  4. ICAR-CNR, University of Calabria, via P. Bucci 41C, 87036, Rende (CS), Italy

    Alfredo Cuzzocrea

  5. Hewlett-Packard Laboratories, 1501 Page Mill Road, 94304, Palo Alto, CA, USA

    Umeshwar Dayal

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Koh, Y.S., Dobbie, G. (2013). Efficient Single Pass Ordered Incremental Pattern Mining. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII. Lecture Notes in Computer Science, vol 7790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37574-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-37574-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37573-6

  • Online ISBN: 978-3-642-37574-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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