DocumentCode :
2254038
Title :
Fuzzy methods for automated intelligent data analysis
Author :
Nauck, Detlef D. ; Spott, Martin ; Azvine, B.
Author_Institution :
Intelligence Syst. Res. Centre, BT, Ipswich, UK
Volume :
1
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
487
Abstract :
Although fuzzy data analysis has increased in popularity within the research community, this technology is rarely found in industrial solutions. In contrast to fuzzy controllers that were rapidly picked up in the 1990´s this has so far not happened for fuzzy data analysis or fuzzy knowledge-based approaches. We believe this is mainly due to not enough easy to use software being available. Software manufactures in the area of data analysis concentrate on statistical approaches and well-known machine learning approaches like decision trees. Typically, neural networks are the only soft computing technique that is sometimes provided. In order to push fuzzy systems and related technology into industrial data analysis applications we need to provide appropriate software. We have developed an IDA platform that automates the data analysis process to a large extent. It uses fuzzy knowledge bases to match user requirements to features of analysis methods and to select, configure and execute IDA processes automatically. Although the platform can use any type of data analysis method we have focused on soft computing methods.
Keywords :
data analysis; decision trees; fuzzy neural nets; knowledge based systems; learning (artificial intelligence); automated intelligent data analysis; decision trees; fuzzy data analysis; fuzzy knowledge- based approach; intelligent data analysis; machine learning; neural network; soft computing method; software manufacture; Appropriate technology; Automatic control; Computer networks; Data analysis; Decision trees; Fuzzy control; Fuzzy systems; Machine learning; Manufacturing industries; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
ISSN :
1098-7584
Print_ISBN :
0-7803-8353-2
Type :
conf
DOI :
10.1109/FUZZY.2004.1375779
Filename :
1375779
Link To Document :
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