WIT Press


A Case Based Reasoning Framework To Extract Knowledge From Data

Price

Free (open access)

Paper DOI

10.2495/DATA000571

Volume

25

Pages

11

Published

2000

Size

1,127 kb

Author(s)

F. Rodriguez, C. Ramos & P. Henriques

Abstract

Data Mining (DM) is the search for relationships and global patterns that exist in databases, but are "hidden" among the vast amounts of data. Re- cent technologies like those embedded in DM tools, allows us to argue that knowledge can be "automatically" obtained from data sources. However, Knowledge Discovery in Databases (KDD) is difficult to perform. Today it is recognised that the effective discovery of new knowledge involves many tasks supported by a heterogeneous suite of tools and requires many deci- sions taken by experts, that must know well many DM techniques and also have a big background knowledge about the area under study. These re- quirements are not common to end-users; this is the reason why we propose in this article a heterogeneous architecture for knowledge extraction from data, whose

Keywords