DocumentCode :
469044
Title :
Quantum Theory: The unified framework for FCM and QC algorithm
Author :
Li, Zhi-hua ; Wang, Shi-Tong
Author_Institution :
Souther Yangtze Univ., Wuxi
Volume :
3
fYear :
2007
fDate :
2-4 Nov. 2007
Firstpage :
1045
Lastpage :
1048
Abstract :
Clustering aims to study the instance distribution in scale-space. Its characteristics are very similar to the particle world in quantum mechanism. The probability wave function describes the distribution of particle, and the Schrodinger equation is the major methodology of solving for wave function when restricted boundary condition is given.Once wave function is confirmed, and the quantum potential serves as the clustering objective function to determine the location of particle distribution. In machine learning, this quantum mechanism implies that we can discover the grouping structures inherent in data. This is the key of quantum clustering, and is the same as the mechanism used in FCM algorithm. In FCM, via the key fuzzy similarity parameter is deduced by the wave function, a important predictability is proposed, which a cryptical wave function is found existing in FCM, finally, a quantum theory interpretation about FCM is presented in this paper.
Keywords :
Schrodinger equation; fuzzy set theory; learning (artificial intelligence); probability; quantum computing; quantum theory; Schrodinger equation; fuzzy similarity parameter; instance distribution; machine learning; particle distribution; probability wave function; quantum theory; restricted boundary condition; Algorithm design and analysis; Clustering algorithms; Notice of Violation; Pattern analysis; Pattern recognition; Quantum computing; Quantum mechanics; Schrodinger equation; Wave functions; Wavelet analysis; Interpretation; Quantum Clustering; Quantum Potential; Quantum Theory; Wave function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1065-1
Electronic_ISBN :
978-1-4244-1066-8
Type :
conf
DOI :
10.1109/ICWAPR.2007.4421586
Filename :
4421586
Link To Document :
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