WIT Press


Evaluating The Scalability Of Data Mining Provider Classifiers

Price

Free (open access)

Paper DOI

10.2495/DATA030621

Volume

29

Pages

10

Published

2003

Size

434 kb

Author(s)

C. L. Curotto & N. F. F. Ebecken

Abstract

Evaluating the scalability of data mining provider classifiers C. L. ~urotto' & N. F. F. beck en^ I CESEC/UFPR - Civil Engineering Graduate Program, Brazil 2 COPPE/UFRJ - Civil Engineering Graduate Program, Brazil Abstract Two classifiers implemented as Data Mining Providers are considered. These providers runs as a stand-alone servers or aggregated with Microsoft@ SQL Server. One of these classifiers is the Microsoft@ Decision Trees algorithm. The other is the Simple Naive Bayes incremental classifier, that supports continuous input attributes, multiple discrete predictable attributes and incremental updating of the training data set. The performance study carried out to verify the scalability of the classifiers includes factors of cardinality (number of training cases), number of input attributes, number of states of the input attributes and number of predictable attributes. 1 Introduction A great effort has been spent to achieve the tight coupling of DM (Data Mining) and OLAP (On-Line Anal

Keywords