This paper presents a supervised variable selection method applied to regression problems. This method selects the variables applying a hierarchical clustering strategy based on information measures. The proposed technique can be applied to single-output regression datasets, and it is extendable to multi-output datasets. For single-output datasets, the method is compared against three other var...

The analysis of changes in water quality in a monitoring network system is important because the sources of pollution vary in time and space. This study utilized analysis of the water quality index calculation, hierarchical cluster analysis, and mapping. This was achieved by assessing the water quality parameters of the samples collected from Galma River in Zaria, Northwestern Nigeria in wet an...

Spatial hierarchy is an appealing strategy for image analysis, but standard hierarchical methods give increasingly sparse, position dependent results with increasing level. This paper endows simple hierarchical techniqueswith positioninvariance by an architectural extension, broadening their scope with no increase in algorithmic complexity. The tree structure is extended intoan exhaustive hiera...

A new robust fuzzy regression clustering method is proposed. We estimate coefficients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a fixed proportion of observations. Assignments to clusters are fuzzy: observations contribute to estimates in more than one single cluster. We describe general criteria for tuning the method. The proposed m...