article

Mining Compressing Sequential Patterns

Authors:

Hoang Thanh Lam,

Fabian Mörchen,

Dmitriy Fradkin,

Toon CaldersAuthors Info & Claims

Statistical Analysis and Data Mining, Volume 7, Issue 1

Pages 34 - 52

https://doi.org/10.1002/sam.11192

Published: 01 February 2014 Publication History

Abstract

Pattern mining based on data compression has been successfully applied in many data mining tasks. For itemset data, the Krimp algorithm based on the minimumdescription length MDL principle was shown to be very effective in solving the redundancy issue in descriptive pattern mining. However, for sequence data, the redundancy issue of the set of frequent sequential patterns is not fully addressed in the literature. In this article, we study MDL-based algorithms for mining non-redundant sets of sequential patterns from a sequence database. First, we propose an encoding scheme for compressing sequence data with sequential patterns. Second, we formulate the problem of mining the most compressing sequential patterns from a sequence database. We show that this problem is intractable and belongs to the class of inapproximable problems. Therefore, we propose two heuristic algorithms. The first of these uses a two-phase approach similar to Krimp for itemset data. To overcome performance issues in candidate generation, we also propose GoKrimp, an algorithm that directly mines compressing patterns by greedily extending a pattern until no additional compression benefit of adding the extension into the dictionary. Since checks for additional compression benefit of an extension are computationally expensive we propose a dependency test which only chooses related events for extending a given pattern. This technique improves the efficiency of the GoKrimp algorithm significantly while it still preserves the quality of the set of patterns. We conduct an empirical study on eight datasets to show the effectiveness of our approach in comparison to the state-of-the-art algorithms in terms of interpretability of the extracted patterns, run time, compression ratio, and classification accuracy using the discovered patterns as features for different classifiers. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013

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Huang GGan WYu P(2024)TaSPM: Targeted Sequential Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/363982718:5(1-18)Online publication date: 28-Feb-2024
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Gandreti SA. IP. S. S(2024)Breadth-First Search Approach for Mining Serial Episodes with Simultaneous EventsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632445(36-44)Online publication date: 4-Jan-2024
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cover image Statistical Analysis and Data Mining

Statistical Analysis and Data Mining Volume 7, Issue 1

February 2014

92 pages

ISSN:1932-1864

EISSN:1932-1872

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John Wiley & Sons, Inc.

United States

Publication History

Published: 01 February 2014

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Cited By

Huang GGan WYu P(2024)TaSPM: Targeted Sequential Pattern MiningACM Transactions on Knowledge Discovery from Data10.1145/363982718:5(1-18)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3639827
Gandreti SA. IP. S. S(2024)Breadth-First Search Approach for Mining Serial Episodes with Simultaneous EventsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632445(36-44)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632445
Sebia HGuyet TAudureau E(2024)SWoTTeD: an extension of tensor decomposition to temporal phenotypingMachine Language10.1007/s10994-024-06545-8113:9(5939-5980)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s10994-024-06545-8
Gandreti SP. S. S(2023)Efficient Depth-First Search Approach for Mining Injective General EpisodesProceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)10.1145/3570991.3571012(1-9)Online publication date: 4-Jan-2023
https://dl.acm.org/doi/10.1145/3570991.3571012
Le HYamada THonda YSakamoto TMatsuo RYamazaki TAraki KYokota H(2023)Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record SystemsACM Transactions on Computing for Healthcare10.1145/35618254:1(1-28)Online publication date: 30-Mar-2023
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Chesani FFrancescomarino CGhidini CLoreti DMaggi FMello PMontali MTessaris S(2023)Process Discovery on Deviant Traces and Other Stranger ThingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323220735:11(11784-11800)Online publication date: 1-Nov-2023
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Lee G(2023)Clustering customer orders in a smart factory using sequential pattern miningThe Journal of Supercomputing10.1007/s11227-023-05351-879:16(18970-18992)Online publication date: 22-May-2023
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Chen JWang PWang WDwyer MDamian DZeller A(2022)Online summarizing alerts through semantic and behavior informationProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510055(1646-1657)Online publication date: 21-May-2022
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Wu YWang XLi YGuo LLi ZZhang JWu X(2022)OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern MiningACM Transactions on Management Information Systems10.1145/347624713:3(1-23)Online publication date: 4-Feb-2022
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Li HWu YHuang Y(2022)On the Feasibility of Anomaly Detection with Fine-Grained Program Tracing EventsJournal of Network and Systems Management10.1007/s10922-021-09635-330:2Online publication date: 1-Apr-2022
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