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Infrequent item mining in multiple data streams

Saha, Budhaditya, Lazarescu, Mihai and Venkatesh, Svetha 2007, Infrequent item mining in multiple data streams, in Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on; ICDM 2007, IEEE, Omaha, NE, pp. 569-574.

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Title Infrequent item mining in multiple data streams
Author(s) Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Lazarescu, Mihai
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE International Conference on Data Mining (17th : 2007 : Omaha, NE)
Conference location Omaha, NE
Conference dates 28-31 Oct. 2007
Title of proceedings Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on; ICDM 2007
Editor(s) IEEE,
Publication date 2007
Conference series IEEE International Conference on Data Mining
Start page 569
End page 574
Total pages 6
Publisher IEEE
Place of publication Omaha, NE
Keyword(s) data mining
data structures
database
intrusion detection
pattern analysis
scalability
statistics
Summary The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based association mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques. © 2007 Crown Copyright.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769530338
9780769530338
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044597

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.