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Cluster-based outlier detection

Author

Listed:
  • Lian Duan
  • Lida Xu
  • Ying Liu
  • Jun Lee

Abstract

Outlier detection has important applications in the field of data mining, such as fraud detection, customer behavior analysis, and intrusion detection. Outlier detection is the process of detecting the data objects which are grossly different from or inconsistent with the remaining set of data. Outliers are traditionally considered as single points; however, there is a key observation that many abnormal events have both temporal and spatial locality, which might form small clusters that also need to be deemed as outliers. In other words, not only a single point but also a small cluster can probably be an outlier. In this paper, we present a new definition for outliers: cluster-based outlier, which is meaningful and provides importance to the local data behavior, and how to detect outliers by the clustering algorithm LDBSCAN (Duan et al. in Inf. Syst. 32(7):978–986, 2007 ) which is capable of finding clusters and assigning LOF (Breunig et al. in Proceedings of the 2000 ACM SIG MOD International Conference on Manegement of Data, ACM Press, pp. 93–104, 2000 ) to single points. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • Lian Duan & Lida Xu & Ying Liu & Jun Lee, 2009. "Cluster-based outlier detection," Annals of Operations Research, Springer, vol. 168(1), pages 151-168, April.
    • Handle: RePEc:spr:annopr:v:168:y:2009:i:1:p:151-168:10.1007/s10479-008-0371-9
    DOI: 10.1007/s10479-008-0371-9
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    Cited by:

    1. Ali Tosyali & Jinho Kim & Jeongsub Choi & Yunyi Kang & Myong K. Jeong, 2020. "New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks," Annals of Operations Research, Springer, vol. 288(1), pages 457-474, May.
    2. Behnam Tavakkol & Myong K. Jeong & Susan L. Albin, 2021. "Validity indices for clusters of uncertain data objects," Annals of Operations Research, Springer, vol. 303(1), pages 321-357, August.
    3. Alexandra CERNIAN & Dorin CARSTOIU & Adriana OLTEANU & Valentin SGARCIU, 2016. "Assessing the Performance of Compression Based Clustering for Text Mining," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 197-210.
    4. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    5. Hae-Sang Park & Jeonghwa Lee & Chi-Hyuck Jun, 2014. "Clustering noise-included data by controlling decision errors," Annals of Operations Research, Springer, vol. 216(1), pages 129-144, May.
    6. Rakin Abrar & Showmitra Kumar Sarkar & Kashfia Tasnim Nishtha & Swapan Talukdar & Shahfahad & Atiqur Rahman & Abu Reza Md Towfiqul Islam & Amir Mosavi, 2022. "Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    7. Philippe Bernard & Najat El Mekkaoui De Freitas & Bertrand B. Maillet, 2022. "A financial fraud detection indicator for investors: an IDeA," Annals of Operations Research, Springer, vol. 313(2), pages 809-832, June.
    8. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 493-518, September.
    9. Andrea Flori & Simone Giansante & Claudia Girardone & Fabio Pammolli, 2021. "Banks’ business strategies on the edge of distress," Annals of Operations Research, Springer, vol. 299(1), pages 481-530, April.
    10. Shouhui Pan & Li Wang & Kaiyi Wang & Zhuming Bi & Siqing Shan & Bo Xu, 2014. "A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 383-397, May.
    11. Aielli, Gian Piero & Caporin, Massimiliano, 2013. "Fast clustering of GARCH processes via Gaussian mixture models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 205-222.

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