Patricia E. N. Lutu

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INTRODUCTION In data mining, sampling may be used as a technique for reducing the amount of data presented to a data mining algorithm. Other strategies for data reduction include dimension reduction, data compression, and discretisation. For sampling, the aim is to draw, from a database, a random sample, which has the same characteristics as the original(More)
R ´ ESUMÉ L'´ echantillonnage pour le minage de large ensemble de données est important pour au moins deux raisons. Le traitement de grande quantité de données a pour résultat une augmentation de la complexité informatique. Le coût de cette complexité additionnelle pourraitêtre non justifiable. D'autre part, l'utilisation de petitséchantillons a pour(More)
Classification modeling is commonly used for predictive data mining to create models (classifiers) that can predict the values of qualitative variables. Ensemble classification is concerned with the creation of many base classifiers which are then combined into one predictive classification model. Positive-versus-negative (pVn) classification has recently(More)
—Data stream mining is the process of applying data mining methods to a data stream in real-time in order to create descriptive or predictive models. Due to the dynamic nature of data streams, new classes may emerge as a data stream evolves, and the concept being modeled may change with time. This gives rise to the need to continuously make revisions to the(More)