Data Preparation in the MineCor KDD Framework

Abstract : Yield enhancement is a key issue in semiconductor manufacturing. Data mining tools can therefore be helpful, by extracting hidden links between numerous complex process control parameters. In order to highlight correlations between such parameters, we developed a complete Knowledge Discovery in Databases (KDD) model, called MineCor. Its mining heart uses a new method derived from association rules programming, based on lectic search and contingency vectors. After recalling these concepts, this paper focuses on data preprocessing and transformation functions, which have an important impact on final results. An overall presentation of these functions, of some significant experimental results and of associated performances are provided and finally discussed.
Type de document :
Communication dans un congrès
IMMM 2011 : The First International Conference on Advances in Information Mining and Management ISBN: 978-1-61208-162-5, Oct 2011, Barcelona, Spain. pp 16-22, 2011
Liste complète des métadonnées

https://hal-emse.ccsd.cnrs.fr/emse-00648333
Contributeur : Christian Ernst <>
Soumis le : lundi 5 décembre 2011 - 15:12:36
Dernière modification le : vendredi 9 mars 2018 - 11:26:03

Identifiants

  • HAL Id : emse-00648333, version 1

Collections

Citation

Christian Ernst, Alain Casali. Data Preparation in the MineCor KDD Framework. IMMM 2011 : The First International Conference on Advances in Information Mining and Management ISBN: 978-1-61208-162-5, Oct 2011, Barcelona, Spain. pp 16-22, 2011. 〈emse-00648333〉

Partager

Métriques

Consultations de la notice

63