A model PM for preprocessing and data mining proper process

Authors:
Anita Wasilewska;Ernestina Menasalvas;Christelle Scharff
Affiliations:
Department of Computer Science, Stony Brook University, NY;Departamento de Lenguajes y Sistemas Informaticos Facultad de Informatica, U.P.M, Madrid, Spain;Computer Science Department, Pace University, New York, NY
Venue:
Transactions on rough sets VI
Year:
2007

Citing 5
Cited 0

Variable precision rough set model

Journal of Computer and System Sciences
From data mining to knowledge discovery: an overview

Advances in knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Mathematical Foundations

Rough Sets: Mathematical Foundations
Induction of Classification Rules by Granular Computing

TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data Mining, as defined in 1996 by Piatetsky-Shapiro ([1]) is a step (crucial, but a step nevertheless) in a KDD (Knowledge Discovery in Data Bases) process. The Piatetsky-Shapiro's definition states that the KDD process consists of the following steps: developing an understanding of the application domain, creating a target data set, choosing the data mining task i.e. deciding whether the goal of the KDD process is classification, regression, clustering, etc..., choosing the data mining algorithm(s), data preprocessing, data mining (DM), interpreting mined patterns, deciding if a re-iteration is needed, and consolidating discovered knowledge.