This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM). The first method uses k -mean clustering to construct a dataset of much smaller size than the original one as the actual input dataset to train SVM. The second method aims at reducing the number of SVs by which the...

We have predicted theoretically and detected in laboratory experiments a tangling clustering of inertial particles in a stably stratified turbulence with imposed mean vertical temperature gradient. In the stratified turbulence a spatial distribution of the mean particle number density is nonuniform due to the phenomenon of turbulent thermal diffusion, i.e., the inertial particles are accumulate...

Image segmentation places an important role in image processing. This segmentation process can be done using various techniques like clustering, thresholding, edge detection and region extraction. This paper gives introduction to image processing operations and clustering process. Then the overview and algorithmic process of each clustering technique such as K-Means clustering, Kernel K-Means c...

Clustering is the one of the efficient datamining techniques for intrusion detection. In clustering algorithm kmean clustering is widely used for intrusion detection. Because it gives efficient results incase of huge datasets. But sometime kmean clustering fails to give best result because of class dominance problem and no class problem. So for removing these problems we are proposing two new a...

Aim. To explore the potential usefulness of the mean, standard deviation (SD), and coefficient of variation (CV = SD/mean) of oximeter oxygen saturations in the clinical care of preterm babies. Methods. This was an exploratory investigation involving 31 preterm babies at 36 weeks postmenstrual age. All babies were healthy, but two were considered to be clinically unstable and required greater a...

The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysi...

The availability of whole-genome sequence data and high-throughput techniques such as DNA microarray enable researchers to monitor the alteration of gene expression by a certain organ or tissue in a comprehensive manner. The quantity of gene expression data can be greater than 30,000 genes per one measurement, making data clustering methods for analysis essential. Biologists usually design expe...

This paper presents an unsupervised texture image segmentation algorithm using reduced Gabor filter set and mean shift clustering. Two criteria are proposed in order to construct a feature space of reduced dimensions for texture image segmentation, based on selected Gabor filter subset from a predefined Gabor filter set. An unsupervised clustering algorithm using the mean shift clustering metho...

We consider applications of clustering techniques, Mean Shift and Self-Organizing Maps, to surface reconstruction (meshing) from scattered point data and review a novel kernel-based clustering method.