DocumentCode :
1854900
Title :
Data mining, unsupervised learning and Bayesian ying-yang theory
Author :
Xu, Lei
Author_Institution :
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Volume :
4
fYear :
1999
fDate :
1999
Firstpage :
2520
Abstract :
A number of unsupervised learning methods or algorithms have been summarized from the perspective of their potential uses in data mining. Major unsupervised learning tasks are then systematically viewed under a unified framework called Bayesian ying-yang (BYY) learning. Furthermore, it is shown systematically how the BYY learning theory can guide us not only to revisit the existing major unsupervised learning methods and results, but also to obtain a number of new methods and results
Keywords :
Bayes methods; data mining; neural nets; principal component analysis; unsupervised learning; Bayesian ying-yang learning; data mining; knowledge discovery; machine learning; principal component analysis; unsupervised learning; Bayesian methods; Clustering algorithms; Curve fitting; Data mining; Data visualization; Delta modulation; Neural networks; Principal component analysis; Surface fitting; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.833469
Filename :
833469
Link To Document :
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