A comparison of two fuzzy clustering techniques

Abstract

In fuzzy clustering, unlike hard clustering, depending on the membership value, a single object may belong exactly to one cluster or partially to more than one cluster. Out of a number of fuzzy clustering techniques Bezdek's Fuzzy C-Means and Gustafson-Kessel clustering techniques are well known where Euclidian distance and Mahalanobis distance are used respectively as a measure of similarity. We have applied these two fuzzy clustering techniques on a dataset of individual differences consisting of fifty feature vectors of dimension (feature) three. Based on some validity measures we have tried to see the performances of these two clustering techniques from three different aspects-first, by initializing the membership values of the feature vectors considering the values of the three features separately one at a time, secondly, by changing the number of the predefined clusters and thirdly, by changing the size of the dataset.

Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data

“…The basic idea of discriminant analysis is to compare the class distances from a sample to centroid each class, and the class with the shortest distance is exactly the samples . The calculation commonly uses two kinds of distances: Euclidean distance and mahalanobis distance . The Euclidean distance is employed in distance identification by SIMCA classification .…”

“… 23 The calculation commonly uses two kinds of distances: Euclidean distance and mahalanobis distance. 24 The Euclidean distance is employed in distance identification by SIMCA classification. 25 The calculation can be divided into two steps.…”