research-article

Mining multidimensional and multilevel sequential patterns

Authors:

Marc Plantevit,

Dominique Laurent,

Maguelonne Teisseire,

Yeow WEI ChoongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 4, Issue 1

Article No.: 4, Pages 1 - 37

https://doi.org/10.1145/1644873.1644877

Published: 18 January 2010 Publication History

Abstract

Multidimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. This framework is different from transactional data as the datasets contain huge volumes of historicized and aggregated data defined over a set of dimensions that can be arranged through multiple levels of granularities. Many tools have been proposed to query the data and navigate through the levels of granularity. However, automatic tools are still missing to mine this type of data in order to discover regular specific patterns. In this article, we present a method for mining sequential patterns from multidimensional databases, at the same time taking advantage of the different dimensions and levels of granularity, which is original compared to existing work. The necessary definitions and algorithms are extended from regular sequential patterns to this particular case. Experiments are reported, showing the significance of this approach.

References

[1]

Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proceedings of the International Conference on Data Engineering (ICDE). IEEE Computer Society, 3--14.

Digital Library

[2]

Ayres, J., Flannick, J., Gehrke, J., and Yiu, T. 2002. Sequential pattern mining using a bitmap representation. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD). AAAI Press, 429--435.

Digital Library

[3]

Beyer, K. and Ramakrishnan, R. 1999. Bottom-up computation of sparse and iceberg cube. In Proceedings of the International Conference on Management of Data (SIGMOD). ACM Press, 359--370.

Digital Library

[4]

Bonchi, F. and Lucchese, C. 2006. On condensed representations of constrained frequent patterns. Knowl. Inform. Syst. 9, 2, 180--201.

Digital Library

[5]

Boulicaut, J.-F., Bykowski, A., and Rigotti, C. 2003. Free-sets: a condensed representation of Boolean data for the approximation of frequency queries. Data Mining Knowl. Disc. 7, 1, 5--22.

Digital Library

[6]

Burdick, D., Calimlim, M., and Gehrke, J. 2001. MAFIA: a maximal frequent itemset algorithm for transactional databases. In Proceedings of the International Conference on Data Engineering (ICDE). IEEE Computer Society, 443--452.

Digital Library

[7]

Calders, T. and Goethals, B. 2002. Mining all non-derivable frequent itemsets. In Proceedings of the European Conference on the Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science, vol. 2431. Springer Verlag, 74--85.

Digital Library

[8]

Calders, T., Rigotti, C., and Boulicaut, J.-F. 2006. A survey on condensed representations for frequent sets. In Proceedings of Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining. Lecture Notes in Computer Science, vol. 3848. Springer Verlag, 64--80.

Digital Library

[9]

Chiu, D.-Y., Wu, Y.-H., and Chen, A. L. P. 2004. An efficient algorithm for mining frequent sequences by a new strategy without support counting. In Proceedings of the International Conference on Data Engineering (ICDE). IEEE Computer Society, 375--386.

Digital Library

[10]

de Amo, S., Furtado, D. A., Giacometti, A., and Laurent, D. 2004. An a priori-based approach for first-order temporal pattern mining. In Proceedings of the Simpósio Brasileiro de Bancos de Dados. 48--62.

[11]

Dietterich, T. and Michalski, R. 1985. Discovering patterns in sequences of events. Artif. Intell. 25, 2, 187--232.

Digital Library

[12]

Han, J. and Fu, Y. 1999. Mining multiple-level association rules in large databases. IEEE Trans. Knowl. Data Engin. 11, 5, 798--804.

Digital Library

[13]

Inmon, W. 2003. Building the Data Warehouse. John Wiley and Sons.

Digital Library

[14]

Mannila, H. and Toivonen, H. 1996. Multiple uses of frequent sets and condensed representations. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD). AAAI Press, 189--194.

[15]

Mannila, H., Toivonen, H., and Verkamo, A. 1995. Discovering frequent episodes in sequences. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD). AAAI Press, 210--215.

[16]

Masseglia, F., Cathala, F., and Poncelet, P. 1998. The PSP approach for mining sequential patterns. In Proceedings of the Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science, vol. 1510. Springer Verlag, 176--184.

Digital Library

[17]

Pasquier, N., Bastide, Y., Taouil, R., and Lakhal, L. 1999a. Discovering frequent closed itemsets for association rules. In Proceedings of the International Conference on Database Theory (ICDT). Lecture Notes in Computer Science, vol. 1540. Springer Verlag, 398--416.

Digital Library

[18]

Pasquier, N., Bastide, Y., Taouil, R., and Lakhal, L. 1999b. Efficient mining of association rules using closed itemset lattices. Inform. Syst. 24, 1, 25--46.

Digital Library

[19]

Pei, J., Han, J., and Mao, R. 2000. Closet: An efficient algorithm for mining frequent closed itemsets. In Proceedings of the SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. ACM Press, 21--30.

[20]

Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. 2004. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Trans. Knowl. Data Engin. 16, 11, 1424--1440.

Digital Library

[21]

Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., and Dayal, U. 2001. Multi-dimensional sequential pattern mining. In Proceedings of the International Conference on Information and Knowledge Management (CIKM). ACM Press, 81--88.

Digital Library

[22]

Plantevit, M., Choong, Y. W., Laurent, A., Laurent, D., and Teisseire, M. 2005. M2SP: Mining sequential patterns among several dimensions. In Proceedings of the European Conference on the Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Artificial Intelligence, vol. 3721. Springer Verlag, 205--216.

[23]

Plantevit, M., Laurent, A., and Teisseire, M. 2006. HYPE: mining hierarchical sequential patterns. In Proceedings of the International Workshop on Data Warehousing and OLAP (DOLAP). ACM Press, 19--26.

Digital Library

[24]

Rashad, S., Kantardzic, M. M., and Kumar, A. 2007. MSP-CACRR: multidimensional sequential patterns-based call admission control and resource reservation for next-generation wireless cellular networks. In Proceedings of the Symposium on Computational Intelligence and Data Mining (CIDM). IEEE Computer Society, 552--559.

[25]

Srikant, R. and Agrawal, R. 1996. Mining sequential patterns: generalizations and performance improvements. In Proceedings of the International Conference on Extending Data Base Technology (EDBT). Lecture Notes in Computer Science, vol. 1057. Springer Verlag, 3--17.

Digital Library

[26]

Stefanowski, J. 2007. Algorithms for context based sequential pattern mining. Fundamenta Informaticae 76, 4, 495--510.

Digital Library

[27]

Stefanowski, J. and Ziembinski, R. 2005. Mining context based sequential patterns. In Proceedings of the Atlantic Web Intelligence Conference (AWIC). Lecture Notes in Computer Science, vol. 3528. Springer Verlag, 401--407.

Digital Library

[28]

Yang, Z., Kitsuregawa, M., and Wang, Y. 2006. Paid: mining sequential patterns by passed item deduction in large databases. In Proceedings of the International Database Engineering and Applications Symposium (IDEAS). IEEE Computer Society, 113--120.

Digital Library

[29]

Yu, C.-C. and Chen, Y.-L. 2005. Mining sequential patterns from multidimensional sequence data. IEEE Trans. Knowl. Data Engin. 17, 1, 136--140.

Digital Library

[30]

Zaki, M. J. 2001. SPADE: an efficient algorithm for mining frequent sequences. Machine Learn. J. Special Issue on Unsupervised Learning 42, 1/2, 31--60.

[31]

Zaki, M. J. 2004. Mining non-redundant association rules. Data Mining Knowl. Discov. 9, 223--248.

Digital Library

[32]

Zaki, M. J. and Hsiao, C.-J. 2002. CHARM: an efficient algorithm for closed itemset mining. In Proceedings of the SIAM International Conference on Data Mining (SDM). SIAM.

[33]

Zhang, C., Hu, K., Chen, Z., Chen, L., and Dong, Y. 2007. ApproxMGMSP: a scalable method of mining approximate multidimensional sequential patterns on distributed system. In Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). Vol. 2. IEEE Computer Society, 730--734.

Digital Library

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Index Terms

Mining multidimensional and multilevel sequential patterns
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 4, Issue 1

January 2010

135 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/1644873

Issue’s Table of Contents

Copyright © 2010 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 January 2010

Accepted: 01 May 2009

Revised: 01 January 2009

Received: 01 August 2008

Published in TKDD Volume 4, Issue 1

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Cited By

Arinik NInterdonato RRoche MTeisseire M(2023)An Evaluation Framework for Comparing Epidemic Intelligence SystemsIEEE Access10.1109/ACCESS.2023.326246211(31880-31901)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3262462
Kolisnyk MKwiatkowski MLeung CZacharias B(2022)Analysis of Multi-Dimensional Road Accident Data for Disaster Management in Smart Cities2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI54793.2022.00022(43-48)Online publication date: 9-Aug-2022
https://dl.acm.org/doi/10.1109/IRI54793.2022.00022
Kwiatkowski MZacharias BLeung CTsu PThomas JKolisnyk MCuzzocrea A(2022)A Fuzzy-Logic Based Multi-Dimensional Analysis of Traffic Incident Data2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE55066.2022.9882787(1-8)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/FUZZ-IEEE55066.2022.9882787
Nam K(2022)Conversion paths of online consumers: A sequential pattern mining approachExpert Systems with Applications10.1016/j.eswa.2022.117253202(117253)Online publication date: Sep-2022
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Kontarinis AZeitouni KMarinica CVodislav DKotzinos D(2021)Towards a semantic indoor trajectory model: application to museum visitsGeoinformatica10.1007/s10707-020-00430-x25:2(311-352)Online publication date: 1-Apr-2021
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Zhang CDu ZZu Y(2020)An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential PatternsWireless Communications & Mobile Computing10.1155/2020/88162282020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/8816228
Daher JBrun A(2020)Handling Item Similarity in Behavioral Patterns through General Pattern Mining2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00092(611-618)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00092
Dagnely PRuette TTourwé TTsiporkova E(2020)Semantically Enriched Multi-level Sequential Pattern Mining for Exploring Heterogeneous Event Log DataIntelligent Systems: Theory, Research and Innovation in Applications10.1007/978-3-030-38704-4_9(203-222)Online publication date: 4-Mar-2020
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BRAHMI H(2019)Post-Mining of Generalized Association Rules from Data Cubes2019 International Conference on Information Networking (ICOIN)10.1109/ICOIN.2019.8718147(153-158)Online publication date: Jan-2019
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Nica CBraud ALe Ber F(2019)RCA-Seq: An original approach for enhancing the analysis of sequential data based on hierarchies of multilevel closed partially-ordered patternsDiscrete Applied Mathematics10.1016/j.dam.2019.02.037Online publication date: Apr-2019
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