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