DocumentCode :
402889
Title :
A knowledge discovery technique for heterogeneous data sources
Author :
Shi, Bai-Sheng ; Shen, Xia-jiong ; Liu, Zongtian
Author_Institution :
Sch. of Comput. Eng., Shanghai Univ., China
Volume :
1
fYear :
2003
fDate :
2-5 Nov. 2003
Firstpage :
293
Abstract :
Knowledge discovery is the non-trivial extraction of implicit, previously unknown and potentially useful information from data. We present a model of how concepts are structured within data sources, after exploring current conceptual structures applied to represent concepts embedded within data sources. These techniques include formal concept analysis (FCA), conceptual graphs (CG), and structured concepts (SC). By developing a hybrid conceptual structure, we intend to capture the key features of FCA, CG, and SC. In the end of this paper, we also present a system architecture for conceptual knowledge discovery.
Keywords :
data mining; data models; conceptual graphs; formal concept analysis; heterogeneous data sources; hybrid conceptual structure; knowledge discovery technique; structured concepts; Character generation; Computer architecture; Data engineering; Data mining; Data models; Knowledge engineering; Large-scale systems; Object oriented databases; Object oriented modeling; Relational databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
Type :
conf
DOI :
10.1109/ICMLC.2003.1264489
Filename :
1264489
Link To Document :
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