Article

IncSpan: incremental mining of sequential patterns in large database

Authors:

Hong Cheng,

Xifeng Yan,

Jiawei HanAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 527 - 532

https://doi.org/10.1145/1014052.1014114

Published: 22 August 2004 Publication History

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Abstract

Many real life sequence databases grow incrementally. It is undesirable to mine sequential patterns from scratch each time when a small set of sequences grow, or when some new sequences are added into the database. Incremental algorithm should be developed for sequential pattern mining so that mining can be adapted to incremental database updates. However, it is nontrivial to mine sequential patterns incrementally, especially when the existing sequences grow incrementally because such growth may lead to the generation of many new patterns due to the interactions of the growing subsequences with the original ones. In this study, we develop an efficient algorithm, IncSpan, for incremental mining of sequential patterns, by exploring some interesting properties. Our performance study shows that IncSpan outperforms some previously proposed incremental algorithms as well as a non-incremental one with a wide margin.

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Cited By

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Rizvee RAhmed CArefin MLeung C(2024)A new tree-based approach to mine sequential patternsExpert Systems with Applications10.1016/j.eswa.2023.122754242(122754)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122754
Huynh HVo BOplatková ZPedrycz W(2023)An Approach for Incremental Mining of Clickstream Patterns as a Service ApplicationIEEE Transactions on Services Computing10.1109/TSC.2023.329494516:6(3892-3905)Online publication date: Nov-2023
https://doi.org/10.1109/TSC.2023.3294945
Hu KGan WHuang GChen GLin J(2023)Incremental Targeted Mining in Sequences2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302500(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302500
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Index Terms

IncSpan: incremental mining of sequential patterns in large database
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2004

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KDD04: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 22 - 25, 2004

WA, Seattle, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Rizvee RAhmed CArefin MLeung C(2024)A new tree-based approach to mine sequential patternsExpert Systems with Applications10.1016/j.eswa.2023.122754242(122754)Online publication date: May-2024
https://doi.org/10.1016/j.eswa.2023.122754
Huynh HVo BOplatková ZPedrycz W(2023)An Approach for Incremental Mining of Clickstream Patterns as a Service ApplicationIEEE Transactions on Services Computing10.1109/TSC.2023.329494516:6(3892-3905)Online publication date: Nov-2023
https://doi.org/10.1109/TSC.2023.3294945
Hu KGan WHuang GChen GLin J(2023)Incremental Targeted Mining in Sequences2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302500(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302500
Diop LDiop CGiacometti ASoulet A(2022)Pattern on demand in transactional distributed databasesInformation Systems10.1016/j.is.2021.101908104:COnline publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.is.2021.101908
Wang LGui LXu P(2022)Incremental sequential patterns for multivariate temporal association rules miningExpert Systems with Applications10.1016/j.eswa.2022.118020207(118020)Online publication date: Nov-2022
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Om Prakash PAbdul Rahman ANagaraj JSivakumar N(2022)Forecasting the User Prediction from Weblogs Using Improved IncSpan AlgorithmSustainable Communication Networks and Application10.1007/978-981-16-6605-6_58(767-777)Online publication date: 17-Jan-2022
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Vo BNguyen HHuynh BLe T(2021)Efficient Methods for Clickstream Pattern Mining on Incremental DatabasesIEEE Access10.1109/ACCESS.2021.31315779(161305-161317)Online publication date: 2021
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Kaushik KReddy PMondal ARalla A(2021)An incremental framework to extract coverage patterns for dynamic databasesInternational Journal of Data Science and Analytics10.1007/s41060-021-00262-4Online publication date: 25-May-2021
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Ishita SAhmed CLeung C(2021)New approaches for mining regular high utility sequential patternsApplied Intelligence10.1007/s10489-021-02536-7Online publication date: 10-Jul-2021
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Roy KMoon MRahman MAhmed CLeung C(2021)Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index StructureAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75765-6_3(29-41)Online publication date: 8-May-2021
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Recommendations

Improvements of incspan: incremental mining of sequential patterns in large database

CanTree: a canonical-order tree for incremental frequent-pattern mining

Incremental mining maximal frequent patterns from univariate uncertain data

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