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Efficient frequent pattern mining over data streams

Published: 26 October 2008 Publication History

Abstract

This paper proposes a prefix-tree structure, called CPS-tree (Compact Pattern Stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. The CPS-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient FP-growth-based [1] mining technique.

References

[1]
Han, J., Pei, J., and Yin Y. 2000. Mining frequent patterns without candidate generation. In Proc. of Int. Conf. on Management of Data. 1--12.
[2]
Koh, J.-L., and Shieh, S.-F. 2004. An efficient approach for maintaining association rules based on adjusting FP-tree structures. In Lee Y.-J., Li J., Whang K.-Y., Lee D. (eds) Proc. of DASFAA 2004. Springer-Verlag, Berlin Heidelberg New York, 417--424.
[3]
Leung, C. K.-S., and Khan, Q. I. 2006. DSTree: A tree structure for the mining of frequent sets from data streams. In Proc. of the 6th Int. Conf. on Data Mining (ICDM). 928--932.
[4]
Tanbeer, S. K., Ahmed, C. F., Jeong, B.-S., and Lee, Y.-K. 2008. CP-tree: a tree structure for single-pass frequent pattern mining. In Proc. of PAKDD, Lect Notes Artif Int, 1022--1027.

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  • (2020)Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional DatabaseHandbook of Research on Applications and Implementations of Machine Learning Techniques10.4018/978-1-5225-9902-9.ch010(173-200)Online publication date: 2020
  • (2019)Association AnalysisData Science10.1016/B978-0-12-814761-0.00006-X(199-220)Online publication date: 2019
  • (2018)The concept of frequent itemset mining for textIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/434/1/012043434(012043)Online publication date: 4-Dec-2018
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    cover image ACM Conferences
    CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management
    October 2008
    1562 pages
    ISBN:9781595939913
    DOI:10.1145/1458082
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 26 October 2008

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    Author Tags

    1. data mining
    2. data stream
    3. frequent pattern

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    CIKM08
    CIKM08: Conference on Information and Knowledge Management
    October 26 - 30, 2008
    California, Napa Valley, USA

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    • (2020)Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional DatabaseHandbook of Research on Applications and Implementations of Machine Learning Techniques10.4018/978-1-5225-9902-9.ch010(173-200)Online publication date: 2020
    • (2019)Association AnalysisData Science10.1016/B978-0-12-814761-0.00006-X(199-220)Online publication date: 2019
    • (2018)The concept of frequent itemset mining for textIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/434/1/012043434(012043)Online publication date: 4-Dec-2018
    • (2018)Rare pattern mining: challenges and future perspectivesComplex & Intelligent Systems10.1007/s40747-018-0085-9Online publication date: 10-Nov-2018
    • (2018)FP-Tree and Its Variants: Towards Solving the Pattern Mining ChallengesProceedings of First International Conference on Smart System, Innovations and Computing10.1007/978-981-10-5828-8_51(535-543)Online publication date: 9-Jan-2018
    • (2017)Balanced Parallel Frequent Pattern Mining over Massive Data Stream2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)10.1109/BigDataService.2017.15(50-59)Online publication date: Apr-2017
    • (2017)A fast algorithm for mining high average-utility itemsetsApplied Intelligence10.1007/s10489-017-0896-147:2(331-346)Online publication date: 1-Sep-2017
    • (2016)CD-TDS: Change detection in transactional data streams for frequent pattern mining2016 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2016.7727383(1554-1561)Online publication date: Jul-2016
    • (2015)Association AnalysisPredictive Analytics and Data Mining10.1016/B978-0-12-801460-8.00006-9(195-216)Online publication date: 2015
    • (2013)Efficient Algorithms for Mining High Utility Itemsets from Transactional DatabasesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2012.5925:8(1772-1786)Online publication date: 1-Aug-2013
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