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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2008: Advances in Knowledge Discovery and Data Mining pp 172–183Cite as

Minimum Variance Associations — Discovering Relationships in Numerical Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5012))

Abstract

The paper presents minimum variance patterns: a new class of itemsets and rules for numerical data, which capture arbitrary continuous relationships between numerical attributes without the need for discretization. The approach is based on finding polynomials over sets of attributes whose variance, in a given dataset, is close to zero. Sets of attributes for which such functions exist are considered interesting. Further, two types of rules are introduced, which help extract understandable relationships from such itemsets. Efficient algorithms for mining minimum variance patterns are presented and verified experimentally.

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Authors and Affiliations

  1. National Institute of Telecommunications, Warsaw, Poland

    Szymon Jaroszewicz

Authors
  1. Szymon Jaroszewicz
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Editor information

Takashi Washio Einoshin Suzuki Kai Ming Ting Akihiro Inokuchi

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© 2008 Springer-Verlag Berlin Heidelberg

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Jaroszewicz, S. (2008). Minimum Variance Associations — Discovering Relationships in Numerical Data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_17

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  • DOI: https://doi.org/10.1007/978-3-540-68125-0_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68124-3

  • Online ISBN: 978-3-540-68125-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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