Fuzzy Linear Discriminant Analysis Research Articles

Elucidation of physiographic and hydrogeological features of the lower Namoi valley using fuzzy k-means classification of EM34 data

In the irrigated regions of New South Wales and Victoria, Australia, secondary soil salinisation is becoming of increasing concern. However, natural resource data are not available to elucidate the threat. What is required is information on stratigraphy and spatial location and quality (i.e. low, intermediate and high salinity) of groundwater. Electromagnetic (EM) induction instruments have been used successfully to obtain this information because they measure bulk electrical conductivity (EC a), which is a function of clay content, mineralogy, salinity and moisture. In this paper, we show how data collected from an EM survey can be used to infer this information in the cotton growing area of the lower Namoi valley, Australia. The survey involved taking EM34 measurements at three coil spacings in the horizontal mode of operation (i.e. 10, 20 and 40 m). In all 1869 locations were visited on an approximate 1-km grid. In order to objectively classify the EC a data into natural resource management units, we used fuzzy k-means (FKM). The classes obtained were subsequently mapped using a method that ensured summation of class membership values to unity and using local ordinary kriging. The use of a confusion index highlighted areas where the collection of additional information may be appropriate. Using fuzzy linear discriminant analysis we found that measurements obtained at the 10 m coil spacing reflect the shallow stratigraphy and physiography, whilst the 40 m coil spacing clearly differentiated parts of the clay plain underlain by saline aquifers. We conclude that the use of EM34 data and fuzzy k-means provide a good and non-destructive approach to representing the lower Namoi valley landscape.

Fuzzy linear discriminant analysis for chemical data sets

This paper introduces a fuzzy linear discriminant analysis (FLDA) for crisp chemical data sets with a few overlapping data points. It is an improvement over ordinary linear discriminant analysis (OLDA). Three data sets have been analyzed, and the experimental results indicate that the performance of FLDA is superior to that of OLDA. In particular, FLDA can give more information about data structure than OLDA.