A new integrated clustering algorithm GFC and switching regressions

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 16, Issue 4, 2002, Pages 433-446

  Wang, Shitong ^a   Jiang, Haifeng ^b   Lu, Hongjun ^b  

^a JIANGNAN UNIVERSITY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Fuzzy clustering; Gravity based clustering; Switching regressions

Indexed keywords

ASYMPTOTIC STABILITY; COMPUTER SIMULATION; FUZZY SETS; MATHEMATICAL MODELS; SET THEORY; THEOREM PROVING;

FUZZY CLUSTERING; GRAVITY-BASED CLUSTERING; SWITCHING REGRESSIONS;

ALGORITHMS;

EID: 0036601352     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001402001769     Document Type: Letter

References (15)

1. Mixture models, outliers, and the em algorithm
2. Maximum-likelihood estimation of a mixture of normal regressions: Starting values and singularities
3. Robust clustering methods: A unified view
4. Algorithms for nonlinear fitting
5. Data mining and kdd: Promise and challenges
6. Wave bargains, threshold effects, and the Phillips curve
7. Switching regression models and fuzzy clustering
8. Maximum likelihood estimates of the parameters of a mixture of two regression lines
9. Discrete parameter variation: Efficient estimation of a switching regression model
10. A possibilistic approach to clustering
11. A new method for mining regression classes in large data sets
12. A new approach to estimating switching regressions
13. Estimating mixtures of normal distributions and switching regressions
14. Mixture densities, maximum likelihood and the em algorithm
15. Mixtures of linear regressions