|
| creator |
Rantzau, Ralf
| | Schwarz, Holger
| | date |
1999-03
| | | | description |
Data mining has been recognised as an essential element of decision
support, which has increasingly become a focus of the database
industry. Like all computationally expensive data analysis
applications, for example Online Analytical Processing (OLAP),
performance is a key factor for usefulness and acceptance in
business. In the course of the CRITIKAL project (Client-Server Rule
Induction Technology for Industrial Knowledge Acquisition from Large
Databases), which is funded by the European Commission, several
kinds of architectures for data mining were evaluated with a strong
focus on high performance. Specifically, the data mining techniques
association rule discovery and decision tree induction were
implemented into a prototype. We present the architecture developed
by the CRITIKAL consortium and compare it to alternative
architectures.
| | format |
application/pdf
| | 135443 Bytes | |