Evaluating The Scalability Of Data Mining Provider Classifiers
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
Related Book
Other papers in this volume
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