Efficient Mining of Outlying Sequential Behavior Patterns

Yifan Xu; Lei Duan; Guicai Xie; Min Fu; Longhai Li; Jyrki Nummenmaa

doi:10.1007/978-3-030-73197-7_22

Efficient Mining of Outlying Sequential Behavior Patterns

Yifan Xu, Lei Duan, Guicai Xie, Min Fu, Longhai Li, Jyrki Nummenmaa

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

Sequential patterns play an important role when observing behavior. For instance, the daily routines and practices of people can be characterized by sequences of activities. These activity sequences, in turn, can be used to find exceptional and changed behavior. Observing students’ behavior changes is an effective approach to find indications of mental health problems, and changes in an elderly person’s daily activities may indicate a weakening health condition. With the availability of behaviour sequential events, outlierness analysis of behavior sequences has been established as a meaningful research problem. This paper considers the mining of outlying behavior patterns (OBP) from sequential behaviors. After discussing the challenges of OBP mining, we present OBP-Miner, a heuristic method that computes OBPs by incorporating various pruning techniques. Empirical studies on two real-world datasets demonstrate that OBP-Miner is effective and efficient.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
Editors	Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Chia-Hui Chang, Jianliang Xu, Wen-Chih Peng, Jen-Wei Huang, Chih-Ya Shen
Publisher	Springer
Pages	325-341
Number of pages	17
ISBN (Print)	9783030731960
DOIs	https://doi.org/10.1007/978-3-030-73197-7_22
Publication status	Published - 2021
Publication type	A4 Article in a conference publication
Event	International Conference on Database Systems for Advanced Applications - Duration: 11 Apr 2021 → 14 Apr 2021

Publication series

Name	Lecture Notes in Computer Science
Volume	12682 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Database Systems for Advanced Applications
Period	11/04/21 → 14/04/21

Keywords

Contrast sequence data mining
Outlierness analysis
Outlying behavior pattern

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-73197-7_22

Cite this

Xu, Y., Duan, L., Xie, G., Fu, M., Li, L., & Nummenmaa, J. (2021). Efficient Mining of Outlying Sequential Behavior Patterns. In C. S. Jensen, E-P. Lim, D-N. Yang, C-H. Chang, J. Xu, W-C. Peng, J-W. Huang, & C-Y. Shen (Eds.), Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings (pp. 325-341). (Lecture Notes in Computer Science; Vol. 12682 LNCS). Springer. https://doi.org/10.1007/978-3-030-73197-7_22

Xu, Yifan ; Duan, Lei ; Xie, Guicai ; Fu, Min ; Li, Longhai ; Nummenmaa, Jyrki. / Efficient Mining of Outlying Sequential Behavior Patterns. Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings. editor / Christian S. Jensen ; Ee-Peng Lim ; De-Nian Yang ; Chia-Hui Chang ; Jianliang Xu ; Wen-Chih Peng ; Jen-Wei Huang ; Chih-Ya Shen. Springer, 2021. pp. 325-341 (Lecture Notes in Computer Science).

@inproceedings{c83f3685a7b04dd88d9984191bf62d26,

title = "Efficient Mining of Outlying Sequential Behavior Patterns",

abstract = "Sequential patterns play an important role when observing behavior. For instance, the daily routines and practices of people can be characterized by sequences of activities. These activity sequences, in turn, can be used to find exceptional and changed behavior. Observing students{\textquoteright} behavior changes is an effective approach to find indications of mental health problems, and changes in an elderly person{\textquoteright}s daily activities may indicate a weakening health condition. With the availability of behaviour sequential events, outlierness analysis of behavior sequences has been established as a meaningful research problem. This paper considers the mining of outlying behavior patterns (OBP) from sequential behaviors. After discussing the challenges of OBP mining, we present OBP-Miner, a heuristic method that computes OBPs by incorporating various pruning techniques. Empirical studies on two real-world datasets demonstrate that OBP-Miner is effective and efficient.",

keywords = "Contrast sequence data mining, Outlierness analysis, Outlying behavior pattern",

author = "Yifan Xu and Lei Duan and Guicai Xie and Min Fu and Longhai Li and Jyrki Nummenmaa",

note = "jufoid=62555; International Conference on Database Systems for Advanced Applications ; Conference date: 11-04-2021 Through 14-04-2021",

year = "2021",

doi = "10.1007/978-3-030-73197-7_22",

language = "English",

isbn = "9783030731960",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "325--341",

editor = "Jensen, {Christian S.} and Ee-Peng Lim and De-Nian Yang and Chia-Hui Chang and Jianliang Xu and Wen-Chih Peng and Jen-Wei Huang and Chih-Ya Shen",

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Xu, Y, Duan, L, Xie, G, Fu, M, Li, L & Nummenmaa, J 2021, Efficient Mining of Outlying Sequential Behavior Patterns. in CS Jensen, E-P Lim, D-N Yang, C-H Chang, J Xu, W-C Peng, J-W Huang & C-Y Shen (eds), Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings. Lecture Notes in Computer Science, vol. 12682 LNCS, Springer, pp. 325-341, International Conference on Database Systems for Advanced Applications, 11/04/21. https://doi.org/10.1007/978-3-030-73197-7_22

Efficient Mining of Outlying Sequential Behavior Patterns. / Xu, Yifan; Duan, Lei; Xie, Guicai; Fu, Min; Li, Longhai; Nummenmaa, Jyrki.

Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings. ed. / Christian S. Jensen; Ee-Peng Lim; De-Nian Yang; Chia-Hui Chang; Jianliang Xu; Wen-Chih Peng; Jen-Wei Huang; Chih-Ya Shen. Springer, 2021. p. 325-341 (Lecture Notes in Computer Science; Vol. 12682 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Efficient Mining of Outlying Sequential Behavior Patterns

AU - Xu, Yifan

AU - Duan, Lei

AU - Xie, Guicai

AU - Fu, Min

AU - Li, Longhai

AU - Nummenmaa, Jyrki

N1 - jufoid=62555

PY - 2021

Y1 - 2021

N2 - Sequential patterns play an important role when observing behavior. For instance, the daily routines and practices of people can be characterized by sequences of activities. These activity sequences, in turn, can be used to find exceptional and changed behavior. Observing students’ behavior changes is an effective approach to find indications of mental health problems, and changes in an elderly person’s daily activities may indicate a weakening health condition. With the availability of behaviour sequential events, outlierness analysis of behavior sequences has been established as a meaningful research problem. This paper considers the mining of outlying behavior patterns (OBP) from sequential behaviors. After discussing the challenges of OBP mining, we present OBP-Miner, a heuristic method that computes OBPs by incorporating various pruning techniques. Empirical studies on two real-world datasets demonstrate that OBP-Miner is effective and efficient.

AB - Sequential patterns play an important role when observing behavior. For instance, the daily routines and practices of people can be characterized by sequences of activities. These activity sequences, in turn, can be used to find exceptional and changed behavior. Observing students’ behavior changes is an effective approach to find indications of mental health problems, and changes in an elderly person’s daily activities may indicate a weakening health condition. With the availability of behaviour sequential events, outlierness analysis of behavior sequences has been established as a meaningful research problem. This paper considers the mining of outlying behavior patterns (OBP) from sequential behaviors. After discussing the challenges of OBP mining, we present OBP-Miner, a heuristic method that computes OBPs by incorporating various pruning techniques. Empirical studies on two real-world datasets demonstrate that OBP-Miner is effective and efficient.

KW - Contrast sequence data mining

KW - Outlierness analysis

KW - Outlying behavior pattern

U2 - 10.1007/978-3-030-73197-7_22

DO - 10.1007/978-3-030-73197-7_22

M3 - Conference contribution

AN - SCOPUS:85104810620

SN - 9783030731960

T3 - Lecture Notes in Computer Science

SP - 325

EP - 341

BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings

A2 - Jensen, Christian S.

A2 - Lim, Ee-Peng

A2 - Yang, De-Nian

A2 - Chang, Chia-Hui

A2 - Xu, Jianliang

A2 - Peng, Wen-Chih

A2 - Huang, Jen-Wei

A2 - Shen, Chih-Ya

PB - Springer

T2 - International Conference on Database Systems for Advanced Applications

Y2 - 11 April 2021 through 14 April 2021

ER -

Xu Y, Duan L, Xie G, Fu M, Li L, Nummenmaa J. Efficient Mining of Outlying Sequential Behavior Patterns. In Jensen CS, Lim E-P, Yang D-N, Chang C-H, Xu J, Peng W-C, Huang J-W, Shen C-Y, editors, Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings. Springer. 2021. p. 325-341. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-73197-7_22

Efficient Mining of Outlying Sequential Behavior Patterns

Abstract

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Keywords

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