TY - GEN
T1 - Data mining as generalization
T2 - A formal model
AU - Menasalvas, Ernestina
AU - Wasilewska, Anita
PY - 2006
Y1 - 2006
N2 - The model we present here formalizes the definition of Data Mining as the process of information generalization. In the model the Data Mining algorithms are defined as generalization operators. We show that only three generalizations operators: classification operator, clustering operator, and association operator are needed to express all Data Mining algorithms for classification, clustering, and association, respectively. The framework of the model allows to describe formally the hybrid systems; combination of classifiers into multi-classifiers, and combination of clustering with classification. We use our framework to show that classification, clustering and association analysis fall into three different generalization categories.
AB - The model we present here formalizes the definition of Data Mining as the process of information generalization. In the model the Data Mining algorithms are defined as generalization operators. We show that only three generalizations operators: classification operator, clustering operator, and association operator are needed to express all Data Mining algorithms for classification, clustering, and association, respectively. The framework of the model allows to describe formally the hybrid systems; combination of classifiers into multi-classifiers, and combination of clustering with classification. We use our framework to show that classification, clustering and association analysis fall into three different generalization categories.
UR - https://www.scopus.com/pages/publications/33749667605
U2 - 10.1007/11539827-6
DO - 10.1007/11539827-6
M3 - Conference contribution
AN - SCOPUS:33749667605
SN - 9783540283157
T3 - Studies in Computational Intelligence
SP - 99
EP - 126
BT - Foundations and Novel Approaches in Data Mining
A2 - Lin, Tsau Young
ER -