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Data Mining and Knowledge Discovery

, Volume 14, Issue 3, pp 367–407 | Cite as

A fast and effective method to find correlations among attributes in databases

  • Elaine P. M. de SousaEmail author
  • Caetano TrainaJr.
  • Agma J. M. Traina
  • Leejay Wu
  • Christos Faloutsos
Article

Abstract

The problem of identifying meaningful patterns in a database lies at the very heart of data mining. A core objective of data mining processes is the recognition of inter-attribute correlations. Not only are correlations necessary for predictions and classifications – since rules would fail in the absence of pattern – but also the identification of groups of mutually correlated attributes expedites the selection of a representative subset of attributes, from which existing mappings allow others to be derived. In this paper, we describe a scalable, effective algorithm to identify groups of correlated attributes. This algorithm can handle non-linear correlations between attributes, and is not restricted to a specific family of mapping functions, such as the set of polynomials. We show the results of our evaluation of the algorithm applied to synthetic and real world datasets, and demonstrate that it is able to spot the correlated attributes. Moreover, the execution time of the proposed technique is linear on the number of elements and of correlations in the dataset.

Keywords

Attribute correlation Attribute selection Intrinsic dimension Fractals 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Elaine P. M. de Sousa
    • 1
    Email author
  • Caetano TrainaJr.
    • 1
  • Agma J. M. Traina
    • 1
  • Leejay Wu
    • 2
  • Christos Faloutsos
    • 2
  1. 1.Department of Computer ScienceUniversity of São Paulo at São CarlosSão CarlosBrazil
  2. 2.Department of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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