DocumentCode :
3120581
Title :
Mining the weights of similarity measure through learning
Author :
Wang, Li-juan ; Wang, Xi-Zhao ; Ha, Ming-Hu ; Gu, Yin-Shan
Author_Institution :
Sch. of Math. & Comput. Sci., Hebei Univ., China
Volume :
4
fYear :
2002
fDate :
4-5 Nov. 2002
Firstpage :
1837
Abstract :
An approach is proposed to minimize a fuzzy feature evaluation index function by genetic algorithms. Since not all evaluation indexes perform well, a cross-entropy is introduced to measure the fuzziness of the evaluation function. Experimental results show that with the function of cross-entropy, a suitable evaluation index is chosen, the fuzziness is reduced and the corresponding clustering is optimized.
Keywords :
data mining; entropy; fuzzy set theory; genetic algorithms; learning (artificial intelligence); pattern clustering; cross-entropy; data mining; fuzzy feature evaluation index function; genetic algorithms; machine learning; optimisation; pattern clustering; similarity measure; transitive closure clustering; Computer science; Entropy; Euclidean distance; Indexes; Machine learning; Mathematics; Paper technology; Particle measurements; Performance evaluation; Weight measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
Type :
conf
DOI :
10.1109/ICMLC.2002.1175358
Filename :
1175358
Link To Document :
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