DocumentCode :
1930622
Title :
Knowledge-oriented clustering for decision support
Author :
Bean, C.L. ; Kambhampati, C.
Author_Institution :
Dept. of Comput. Sci., Hull Univ., UK
Volume :
4
fYear :
2003
fDate :
20-24 July 2003
Firstpage :
3244
Abstract :
Cluster analysis is traditionally an unsupervised data. reduction technique. However, by unifying ideas from both cluster analysis and rough set theory, the inherent structure of a data set can be expressed as a rule set which, in turn, can be modified in a supervised manner to obtain a decision rule set with minimal ambiguity. This process of encasing knowledge extraction in an algorithmic framework results in an optimal process for decision support.
Keywords :
decision support systems; knowledge acquisition; knowledge based systems; pattern clustering; rough set theory; statistical analysis; algorithmic framework; cluster analysis; data set; decision rule set; decision support; knowledge extraction; knowledge-oriented clustering; rough set theory; Clustering algorithms; Computer science; Data mining; Decision making; Error analysis; Humans; Information retrieval; Knowledge representation; Mirrors; Set theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1224093
Filename :
1224093
Link To Document :
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