Mixtures of linear regressions
Computational Statistics & Data Analysis
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Relational mountain (density) clustering method and web log analysis
International Journal of Intelligent Systems
A modified mountain clustering algorithm
Pattern Analysis & Applications
ϵ-insensitive fuzzy c-regression models: introduction to ϵ-insensitive fuzzy modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
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Since Quandt [The estimation of the parameters of a linear regression system obeying two separate regimes, Journal of the American Statistical Association 53 (1958) 873-880] initiated the research on 2-regressions analysis, switching regression had been widely studied and applied in psychology, economics, social science and music perception. In fuzzy clustering, the fuzzy c-means (FCM) is the most commonly used algorithm. Hathaway and Bezdek [Switching regression models and fuzzy clustering, IEEE Transactions on Fuzzy Systems 1 (1993) 195-204] embedded FCM into switching regression where it was called fuzzy c-regressions (FCR). However, the FCR always depends heavily on initial values. In this paper, we propose a mountain c-regressions (MCR) method for solving the initial-value problem. First, we perform data transformation for the switching regression data set, and then implement the modified mountain clustering on the transformed data to extract c cluster centers. These extracted c cluster centers in the transformed space will correspond to c regression models in the original data set. The proposed MCR method can form well-estimated c regression models for switching regression data sets. According to the properties of transformation, the proposed MCR is also robust to noise and outliers. Several examples show the effectiveness and superiority of our proposed method.