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Vers des solutions adaptatives et génériques pour l'extraction de motifs intéressants dans les données

Abstract : The discovery of frequent patterns is one of the problems in data mining. To better understand the influence of the data on the algorithms, we present an experimental study of data sets commonly used by the community. This study lead to a new classification of data based on edge: stable and consistent with the performance of algorithms. Despite the large number of studies and a theoretical framework for extracting interesting patterns problems, the use of these algorithms for solving problems "equivalent" is uncommon and remains difficult. Given these limitations, we propose a generic algorithm for discovering interesting patterns borders, called ABS (Adaptive Search borders), dynamically adapting its strategy to data. In addition, a generic component library C + + has been proposed to facilitate the development of software solutions for this family of problems
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https://theses.hal.science/tel-00844480
Contributor : Camille Meyer Connect in order to contact the contributor
Submitted on : Monday, July 15, 2013 - 12:33:45 PM
Last modification on : Monday, December 13, 2021 - 3:06:39 AM
Long-term archiving on: : Wednesday, October 16, 2013 - 4:15:38 AM

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  • HAL Id : tel-00844480, version 1

Citation

Frédéric Flouvat. Vers des solutions adaptatives et génériques pour l'extraction de motifs intéressants dans les données. Algorithme et structure de données [cs.DS]. Université Blaise Pascal - Clermont-Ferrand II, 2006. Français. ⟨NNT : 2006CLF21710⟩. ⟨tel-00844480⟩

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