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Infrequent item mining in multiple data streams

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conference contribution
posted on 2007-01-01, 00:00 authored by Budhaditya Saha, M Lazarescu, Svetha VenkateshSvetha Venkatesh
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.

History

Pagination

569 - 574

Location

Omaha, NE

Open access

  • Yes

Start date

2007-10-28

End date

2007-10-31

ISBN-13

9780769530338

ISBN-10

0769530338

Language

eng

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.

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2007, IEEE

Editor/Contributor(s)

IEEE

Title of proceedings

Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on; ICDM 2007

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