A partitional and a fuzzy clustering algorithm are compared in this paper in terms of accuracy, robustness and efficiency. 3D position data extracted from a stereo-vision system have to be clustered to use them in a tracking application in which a particle filter is the kernel of the estimation task. ‘K-Means’ and ‘Subtractive’ algorithms have been modified and enriched with a validation proces...

In general, road lane detection from a traffic surveillance camera is done by the analysis of geometric shapes of the road. Thus, Hough transform or B-snake technology is preferred to intelligent pattern matching or machine learning such as neural network. However, we insist that the feasibility of using intelligent technique in this area is quite undervalued. In this paper, we first divide the...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds...

High dimensions have a devastating effect on the FCM algorithm and similar algorithms. One effect is that the prototypes run into the centre of gravity of the entire data set. The objective function must have a local minimum in the centre of gravity that causes FCM’s behaviour. In this paper, examine this problem. This paper answers the following questions: How many dimensions are necessary to ...

In speaker verification, a claimed speaker’s score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker’s and the impostors’ likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased normalisation method to find a better s...

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering crit...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values. For example, a particular datum x(k) might be incomplete, having the form x(k)=(25...

Most existing fuzzy clustering approaches group objects in a dataset based on either a feature-vector representation of each object, or pairwise relationship representation between each pair of objects. However, when both forms of data representations from different descriptions are available for a given dataset, we believe that a dual and cooperative analysis of feature-vectors (vector data) a...

Researchers have observed that multistage clustering can accelerate convergence and improve clustering quality. Two-stage and two-phase fuzzy C-means (FCM) algorithms have been reported. In this paper, we demonstrate that the FCM clustering algorithm can be improved by the use of static and dynamic single-pass incremental FCM procedures. Keywords-Clustering; Fuzzy C-Means Clustering; Incrementa...