Article

On the estimation of frequent itemsets for data streams: theory and experiments

Authors:

Pierre-Alain Laur,

Richard Nock,

Jean-Emile Symphor,

Pascal PonceletAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 327 - 328

https://doi.org/10.1145/1099554.1099650

Published: 31 October 2005 Publication History

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Abstract

In this paper, we devise a method for the estimation of the true support of itemsets on data streams, with the objective to maximize one chosen criterion among {precision, recall} while ensuring a degradation as reduced as possible for the other criterion. We discuss the strengths, weaknesses and range of applicability of this method that relies on conventional uniform convergence results, yet guarantees statistical optimality from different standpoints.

References

[1]

P.-A. Laur, J.-E. Symphor, R. Nock, and P. Poncelet. Statistical Supports for Frequent Itemsets on Data Streams. In P. Perner and I. Atsushi, editors, Machine Learning and Data Mining in Pattern Recognition. Springer Verlag LNCS 3587 (to appear), 2005.

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[2]

H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1:241--258, 1997.

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[3]

D. McAllester. Some PAC-Bayesian theorems. Machine Learning, 37:355--363, 1999.

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[4]

V. Vapnik. Statistical Learning Theory. John Wiley, 1998.

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Index Terms

On the estimation of frequent itemsets for data streams: theory and experiments
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval

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Published In

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2005

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data stream mining

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CIKM05

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CIKM05: Conference on Information and Knowledge Management

October 31 - November 5, 2005

Bremen, Germany

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Abstract

References

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An efficient algorithm for mining temporal high utility itemsets from data streams

Mining frequent patterns across multiple data streams

Mining of Frequent Itemsets from Streams of Uncertain Data

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