A survey of kernel and spectral methods for clustering

Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods. The aim of this paper is to present a survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters. The presented kernel clustering methods are the kernel version of many classical clustering algorithms, e.g., K-means, SOM and neural gas. Spectral clustering arise from concepts in spectral graph theory and the clustering problem is configured as a graph cut problem where an appropriate objective function has to be optimized. An explicit proof of the fact that these two paradigms have the same objective is reported since it has been proven that these two seemingly different approaches have the same mathematical foundation. Besides, fuzzy kernel clustering methods are presented as extensions of kernel K-means clustering algorithm. (C) 2007 Pattem Recognition Society. Published by Elsevier Ltd. All rights reserved

Improved support vector clustering algorithm for color image segmentation

Color image segmentation has attracted more and more attention in various application fields during the past few years. Essentially speaking, color image segmentation problem is a process of clustering according to the color of pixels. But, traditional clustering methods do not scale well with the number of training sample, which limits the ability of handling massive data effectively. With the utilization of an improved approximate Minimum Enclosing Ball algorithm, this article develops an fast support vector clustering algorithm for computing the different clusters of given color images in kernel-introduced space to segment the color images. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to other popular algorithms, it has the competitive performances both on training time and accuracy. Color image segmentation experiments on both synthetic and real-world data sets demonstrate the validity of the proposed algorithm

Comparison of Telecommunication Markets in Europe using Multivariate Statistical Analysis

Common problem in valuation of telecommunication companies is finding comparable data and markets for valuation. The aim of this work was to identify comparable markets for the telecommunication market in Europe. A method for comparison of the markets based on the Multivariate Statistical Analysis was presented. The study covers twenty-two European countries. Using taxonomic measures, these countries were divided into five groups, taking into account the following variables: average monthly service cost of the fixed Internet, average cost of the mobile usage, and average cost of the fixed telephony usage. Within individual groups, the costs of telecommunications services are less diverse than in the entire population; their members can be considered comparable markets. The same method can be used for comparing markets in cases of enterprise valuations in the telecommunication sector, and also in analysis of their level of development

A Review on Methods of Identifying and Counting Aedes Aegypti Larvae using Image Segmentation Technique

Aedes aegypti mosquitoes are a small slender fly insect that spreads the arbovirus from flavivirus vector through its sucking blood. An early detection of this species is very important because once these species turn into adult mosquitoes a population control becomes more complicated. Things become worse when difficult access places like water storage tank becomes one of the breeding favorite places for Aedes aegypti mosquitoes. Therefore, there is a need to help the field operator during the routine inspection for an automated identification and detection of Aedes aegypti larvae, especially at difficult access places. This paper reviews different methodologies that have been used by various researchers in identifying and counting Aedes aegypti. The objective of the review was to analyze the techniques and methods in identifying and counting the Aedes Aegypti larvae of various fields of study from 2008 and above by taking account their performance and accuracy. From the review, thresholding method was the most widely used with high accuracy in image segmentation followed by hidden Markov model, histogram correction and morphology operation region growing

Design and Simulation of a Novel Clustering based Fuzzy Controller for DC Motor Speed Control

This research article proposes the speed control of a DC Motor (series as well as shunt motor). The noveltyof this article lies in the application of kernel based hybrid c-means clustering (KPFCM) in the design offuzzy controller for the speed control of DC Motor. The proposed approach provides a mechanism to obtainthe reduced rule set covering the whole input/output space as well as the parameters of membershipfunctions for each input variable. The performance of the proposed clustering based fuzzy logic controlleris compared with that of its corresponding conventional fuzzy logic controller in terms of severalperformance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) andintegral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improvedperformance over its conventional counterpart. Also it shows that the proposed controller scheme givesmuch faster results as it reduces the computational time.Keywords: DC Motor, Fuzzy control, Kernel, Clustering, Validity inde