skip to main content
10.1145/1516241.1516316acmconferencesArticle/Chapter ViewAbstractPublication PagesicuimcConference Proceedingsconference-collections
research-article

A new data structure for asynchronous periodic pattern mining

Published: 15 February 2009 Publication History

Abstract

The periodic pattern mining is to discover valid periodic patterns in a time-related dataset. Previous studies mostly concern the synchronous periodic patterns. There are many methods for mining periodic patterns proposed in literature. Nevertheless, asynchronous periodic pattern mining gradually receives more and more attention recently. In this paper, we propose an efficient linked structure and the OEOP algorithm to discover all kinds of valid segments in each single event sequence. Then, refer to the general model of asynchronous periodic pattern mining proposed by Huang and Chang, we combine these valid segments found by OEOP into 1-patterns with multiple events, multiple patterns with multiple events and asynchronous periodic patterns. Besides, we implement these algorithms on two real datasets. The experimental results show that these algorithms have the good performance and scalability.

References

[1]
R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," In Proceedings of the 20th International Conference Very Large Data Bases (VLDB'94), pp. 487--499, 1994.
[2]
J. Han, J. Pei, and Y. Yin, "Mining frequent patterns without candidate generation," In Proceedings of ACM SIGMOD International Conference Management of Data (SIGMOD '00), pp. 1--12, 2000.
[3]
R. Agrawal and R. Srikant, "Mining sequential patterns," In Proceedings of the 11th International Conference Data Eng. (ICDE '95), pp. 3--14, 1995.
[4]
J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu, "Mining sequential patterns by pattern-growth: The PrefixSpan approach," IEEE Transactions on Knowledge and Data Engineering, vol. 16, pp. 1424--1440, 2004.
[5]
H. Mannila, H. Toivonen, and A. I. Verkamo, "Discovering frequent episodes in sequences," In Proceedings of the 1st International Conference Knowledge Discovery and Data Mining, pp. 210--215, 1995.
[6]
H. Mannila, H. Toivonen, and A. I. Verkamo, "Discovering generalized episodes using minimal occurrences," In Proceedings of the 2nd International Conference Knowledge Discovery and Data Mining, pp. 146--151, 1996.
[7]
H. Mannila, H. Toivonen, and A. I. Verkamo, "Discovering frequent episodes in event sequences," Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 259--289, 1997.
[8]
H. J. Loether and D. G. McTavish, "Descriptive and inferential statistics: an Introduction," Allyn and Bacon, 1993
[9]
J. Han, W. Gong, and Y. Yin, "Mining segment-wise periodic patterns in time-related databases," In Proceedings of the 4th ACM SIGKDD International Conference Knowledge Discovery and Data Mining (KDD'98), pp. 214218, 1998.
[10]
J. Han, G. Dong, and Y. Yin, "Efficient mining partial periodic patterns in time series database," In Proceedings of the 15th International Conference Data Eng. (ICDE '99), pp. 106--115, 1999.
[11]
J. Yang, W. Wang, and P. S. Yu, "Mining asynchronous periodic patterns in time series data," In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 275--279, 2000.
[12]
J. Yang, W. Wang, and P. S. Yu, "Infominer: mining surprising periodic patterns," In Proceedings of ACM SIGKDD International Conference Knowledge Discovery and Data Mining (KDD'01), San Francisco CA, USA, pp. 395--400, 2001.
[13]
J. Yang, W. Wang, and P. S. Yu, "InfoMiner+: mining surprising periodic patterns with gap penalties," In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), pp. 725--728, 2002.
[14]
J. Yang, W. Wang, and P. S. Yu, "Mining asynchronous periodic patterns in time series data," IEEE Transactions on Knowledge and Data Engining, vol. 15, no. 3, pp. 613--628, 2003.
[15]
K.-Y. Huang and C.-H. Chang, "SMCA: a general model for mining asynchronous periodic patterns in temporal databases," IEEE Transactions on Knowledge and Data Engineering, vol. 17, pp. 774--785, 2005.

Cited By

View all
  • (2019)An innovative model to mine asynchronous periodic pattern of moving objectsMultimedia Tools and Applications10.1007/s11042-018-6752-478:7(8943-8964)Online publication date: 1-Apr-2019
  • (2018)Hierarchical trajectory clustering for spatio-temporal periodic pattern miningExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.09.04092:C(1-11)Online publication date: 1-Feb-2018
  • (2017)A hash-based algorithm for mining non-redundant asynchronous periodic patternsInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2016.08186411:3(205-228)Online publication date: 1-Jan-2017
  • Show More Cited By

Index Terms

  1. A new data structure for asynchronous periodic pattern mining

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
    February 2009
    704 pages
    ISBN:9781605584058
    DOI:10.1145/1516241
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 February 2009

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. asynchronous sequence
    2. data mining
    3. periodic pattern

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    ICUIMC '09
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 251 of 941 submissions, 27%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2019)An innovative model to mine asynchronous periodic pattern of moving objectsMultimedia Tools and Applications10.1007/s11042-018-6752-478:7(8943-8964)Online publication date: 1-Apr-2019
    • (2018)Hierarchical trajectory clustering for spatio-temporal periodic pattern miningExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.09.04092:C(1-11)Online publication date: 1-Feb-2018
    • (2017)A hash-based algorithm for mining non-redundant asynchronous periodic patternsInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2016.08186411:3(205-228)Online publication date: 1-Jan-2017
    • (2016)A comprehensive study on periodicity mining algorithms2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)10.1109/ICGTSPICC.2016.7955365(567-575)Online publication date: Dec-2016
    • (2015)Periodic Pattern Mining for Spatio-Temporal Trajectories: A Survey2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE.2015.92(306-313)Online publication date: Nov-2015
    • (2014)Performance Analysis of Asynchronous Periodic Pattern Mining AlgorithmsICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II10.1007/978-3-319-03095-1_10(87-95)Online publication date: 2014

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media