Fuzzy kernel clustering self-adaptive algorithm

Kongzhi yu Juece/Control and Decision

Volumn 19, Issue 5, 2004, Pages 595-597

  Li, Kan ^a   Liu, Yu Shu ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Feature space; Fuzzy C means; Mercer kernel; Validity measure function

Indexed keywords

DATA MINING; FUZZY SETS; LEARNING SYSTEMS;

FEATURE SPACE; FUZZY C MEANS; FUZZY CLUSTERING ALGORITHMS; MERCER KERNEL; VALIDITY MEASURE FUNCTION;

ADAPTIVE ALGORITHMS;

EID: 3442877646     PISSN: 10010920     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (5)

1. MacQueen J. Some methods for classification and analysis of multivariate observations [A]. Proc 5th Berkeley Symposium in Mathematics, Statistics, Probability [C]. California, 1967. 281-297.
2. Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms [M]. New York: Plenum Press, 1981
3. Scholkopf B, Mika S, Burges C, et al. Input space versus feature space in kernel-based methods [J]. IEEE Trans on Neural Networks, 1999, 10(5): 1000-1017.
4. Kamel S Mohamed. New algorithms for solving the fuzzy c-means clustering problem [J]. Pattern Recognition, 1994, 27(3): 421-428.
5. Girolami M. Mercer kernel based clustering in feature space [J]. IEEE Trans on Neural Network, 2002, 13(3): 780-784.