A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
A class of rational cardinality-based similarity measures
Journal of Computational and Applied Mathematics
Fuzzy Modeling for Control
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fuzzy c-Mean Algorithm Based on Complete Mahalanobis Distances and Separable Criterion
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Soft Computing for Reservoir Characterization and Modeling
Soft Computing for Reservoir Characterization and Modeling
Clustering algorithms based on volume criteria
IEEE Transactions on Fuzzy Systems
Fuzzy clustering with volume prototypes and adaptive cluster merging
IEEE Transactions on Fuzzy Systems
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Global regression models do not accurately reflect the spatial heterogeneity which characterises most geo-environmental variables. In analysing the relationships between such variables, an approach is required which allows the model parameters to vary spatially. This paper proposes a new framework for exploring local relationships between geo-environmental variables. The method is based on extended objective function based fuzzy clustering with the environmental parameters estimated through on a locally weighted regression analysis. The case studies and prediction evaluations show that the fuzzy algorithm yields well-fitted models and accurate predictions. In addition to an increased accuracy of prediction relative to the widely-used geographically weighted regression (GWR), the proposed algorithm provides the search radius (bandwidth) and weights for local estimation directly from the data. The results suggest that the method could be employed effectively in tackling real world kernel-based modelling problems.