Article

CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns

Authors:

Carson Kai-Sang Leung,

Quamrul I. Khan,

Tariqul HoqueAuthors Info & Claims

ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining

Pages 274 - 281

https://doi.org/10.1109/ICDM.2005.38

Published: 27 November 2005 Publication History

Abstract

Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (Canonical-order Tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree.

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Cited By

Nguyen T(2018)Mining incrementally closed item sets with constructive pattern setExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.01.003100:C(41-67)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1016/j.eswa.2018.01.003
Kusumakumari VSherigar DChandran RPatil N(2017)Frequent pattern mining on stream data using Hadoop CanTree-GTreeProcedia Computer Science10.1016/j.procs.2017.09.134115:C(266-273)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.09.134
Kim JHwang B(2016)Real-time stream data mining based on CanTree and GtreeInformation Sciences: an International Journal10.1016/j.ins.2016.06.018367:C(512-528)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.06.018
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CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns

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cover image Guide Proceedings

ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining

November 2005

837 pages

ISBN:0769522785

Publisher

IEEE Computer Society

United States

Publication History

Published: 27 November 2005

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Cited By

Nguyen T(2018)Mining incrementally closed item sets with constructive pattern setExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.01.003100:C(41-67)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1016/j.eswa.2018.01.003
Kusumakumari VSherigar DChandran RPatil N(2017)Frequent pattern mining on stream data using Hadoop CanTree-GTreeProcedia Computer Science10.1016/j.procs.2017.09.134115:C(266-273)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.09.134
Kim JHwang B(2016)Real-time stream data mining based on CanTree and GtreeInformation Sciences: an International Journal10.1016/j.ins.2016.06.018367:C(512-528)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.06.018
Qin XAhsan RLin XRundensteiner EWard M(2014)iPARASProceedings of the 3rd International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications - Volume 3610.5555/2999973.2999985(149-165)Online publication date: 24-Aug-2014
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Priya RA.Vadivel Thakur R(2012)Maximal Pattern Mining Using Fast CP-Tree for Knowledge DiscoveryInternational Journal of Information Systems and Social Change10.4018/jissc.20120101063:1(56-74)Online publication date: 1-Jan-2012
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Totad SGeeta RPrasad Reddy P(2012)Batch incremental processing for FP-tree construction using FP-Growth algorithmKnowledge and Information Systems10.1007/s10115-012-0514-933:2(475-490)Online publication date: 1-Nov-2012
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Hernández-León RHernández-Palancar JCarrasco-Ochoa JMartínez-Trinidad J(2010)Algorithms for mining frequent itemsets in static and dynamic datasetsIntelligent Data Analysis10.5555/1839514.183952414:3(419-435)Online publication date: 1-Aug-2010
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Leung CBrajczuk D(2010)Efficient algorithms for the mining of constrained frequent patterns from uncertain dataACM SIGKDD Explorations Newsletter10.1145/1809400.180942511:2(123-130)Online publication date: 27-May-2010
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Leung CBrajczuk DDesai BSacca DGreco S(2009)Mining uncertain data for constrained frequent setsProceedings of the 2009 International Database Engineering & Applications Symposium10.1145/1620432.1620444(109-120)Online publication date: 16-Sep-2009
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Leung CBrajczuk D(2009)Efficient algorithms for mining constrained frequent patterns from uncertain dataProceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data10.1145/1610555.1610557(9-18)Online publication date: 28-Jun-2009
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