2009, English, Article edition: A Lower Bound on the Sample Size needed to perform a Significant Frequent Pattern Mining Task Jacquemont, Stéphanie; Jacquenet, François; Sebban, Marc

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Physical Description
  • text
Published
  • HAL - CCSD
  • 2009
Language
  • English

Edition details

Title
  • A Lower Bound on the Sample Size needed to perform a Significant Frequent Pattern Mining Task
Author
  • Jacquemont, Stéphanie
  • Jacquenet, François
  • Sebban, Marc
Published
  • HAL - CCSD
  • 2009
Physical Description
  • text
Subjects
Notes
  • During the past few years, the problem of assessing the statistical significance of frequent patterns extracted from a given set S of data has received much attention. Considering that S always consists of a sample drawn from an unknown underlying distribution, two types of risks can arise during a frequent pattern mining process: accepting a false frequent pattern or rejecting a true one. In this context, many approaches presented in the literature assume that the dataset size is an application-dependent parameter. In this case, there is a trade-off between both errors leading to solutions that only control one risk to the detriment of the other one. On the other hand, many sampling-based methods have attempted to determine the optimal size of S ensuring a good approximation of the original (potentially infinite) database from which S is drawn. However, these approaches often resort to Chernoff bounds that do not allow the independent control of the two risks. In this paper, we overcome the mentioned drawbacks by providing a lower bound on the sample size required to control both risks and achieve a significant frequent pattern mining task.
  • HAL:hal-00381667, version 1
  • HAL:http:/​/​hal.archives-ouvertes.fr/​hal-00381667/​en/​
  • DOI:10.1016/​j.patrec.2009.05.002
Language
  • English
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