Article

Classification rule mining for a stream of perennial objects

Authors:

Zaigham Faraz Siddiqui,

Myra SpiliopoulouAuthors Info & Claims

RuleML'2011: Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications

Pages 281 - 296

Published: 19 July 2011 Publication History

Abstract

We study classification over a slow stream of complex objects like customers or students. The learning task must take into account that an object's label is influenced by incoming data from adjoint, fast streams of transactions, e.g. customer purchases or student exams, and that this label may even change over time. This task involves combining the streams, and exploiting associations between the target label and attribute values in the fast streams. We propose a method for the discovery of classification rules over such a confederation of streams, and we use it to enhance a decision tree classifier. We show that the new approach has competitive predictive power while building much smaller decision trees than the original classifier.

References

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Aydin, T., Güvenir, H.A.: Modeling interestingness of streaming classification rules as a classification problem. In: Savaci, F.A. (ed.) TAINN 2005. LNCS (LNAI), vol. 3949, pp. 168-176. Springer, Heidelberg (2006).

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Siddiqui, Z.F., Spiliopoulou, M.: Combining multiple interrelated streams for incremental clustering. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 535-552. Springer, Heidelberg (2009).

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Siddiqui, Z.F., Spiliopoulou, M.: Stream clustering of growing objects. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 433-440. Springer, Heidelberg (2009).

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Siddiqui, Z.F., Spiliopoulou, M.: Tree induction over perennial objects. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 640-657. Springer, Heidelberg (2010).

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Cited By

Krempl GŽliobaite IBrzeziński DHüllermeier ELast MLemaire VNoack TShaker ASievi SSpiliopoulou MStefanowski J(2014)Open challenges for data stream mining researchACM SIGKDD Explorations Newsletter10.1145/2674026.267402816:1(1-10)Online publication date: 25-Sep-2014
https://dl.acm.org/doi/10.1145/2674026.2674028
Zimmermann MNtoutsi ESpiliopoulou MCho YShin SKim SHung CHong J(2014)Adaptive semi supervised opinion classifier with forgetting mechanismProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2555039(805-812)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.1145/2554850.2555039

Index Terms

Classification rule mining for a stream of perennial objects
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

RuleML'2011: Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications

July 2011

383 pages

ISBN:9783642225451

Editors:
Nick Bassiliades
Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece
,
Guido Governatori
NICTA, Queensland Research Laboratory, St. Lucia, QLD, Australia
,
Adrian Paschke
Freie Universität Berlin, Computer Science Department, Corporate Semantic Web, Berlin, Germany

Sponsors

University Bologna: University Bologna
Corporate Semantic Web: Corporate Semantic Web
Model Systems: Model Systems
NICTA: National Information and Communications Technology Australia
Vulcan

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 19 July 2011

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Cited By

Krempl GŽliobaite IBrzeziński DHüllermeier ELast MLemaire VNoack TShaker ASievi SSpiliopoulou MStefanowski J(2014)Open challenges for data stream mining researchACM SIGKDD Explorations Newsletter10.1145/2674026.267402816:1(1-10)Online publication date: 25-Sep-2014
https://dl.acm.org/doi/10.1145/2674026.2674028
Zimmermann MNtoutsi ESpiliopoulou MCho YShin SKim SHung CHong J(2014)Adaptive semi supervised opinion classifier with forgetting mechanismProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2555039(805-812)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.1145/2554850.2555039

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