<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Unsupervised learning is one of the major research areas in machine learning, while kernel methods provide eficient solutions for various statistical learning problems. In this paper we propose a kernel based clustering algorithm that uses the Particle Swarm Optimization technique and discriminant functions. The method represents a general framework for solving the clustering problem: once an appropriate clustering validation index is chosen for a given class of datasets, the method performs very well in solving the problem. The method automatically detects the clusters in a given dataset and also, automatically estimates the number of clusters. Due to the use of kernel functions, our approach can be used for both linearly separable and linearly non-separable clusters. Since our algorithm uses the Particle Swarm Optimization technique, parallel computation may be used, if necessary. We evaluate our method on various datasets and we discuss its capabilities. </p></abstract>

Unsupervised learning is one of the major research areas in machine learning, while kernel methods provide eficient solutions for various statistical learning problems. In this paper we propose a kernel based clustering algorithm that uses the Particle Swarm Optimization technique and discriminant functions. The method represents a general framework for solving the clustering problem: once an appropriate clustering validation index is chosen for a given class of datasets, the method performs very well in solving the problem. The method automatically detects the clusters in a given dataset and also, automatically estimates the number of clusters. Due to the use of kernel functions, our approach can be used for both linearly separable and linearly non-separable clusters. Since our algorithm uses the Particle Swarm Optimization technique, parallel computation may be used, if necessary. We evaluate our method on various datasets and we discuss its capabilities.

A Kernel Based Clustering Algorithm using Particle Swarm Optimization

Faculty of Mathematics and Computer Science University of Bucharest, Academiei no. 14, Bucharest, Romania

Faculty of Automatic Control and Computers University Politehnica of Bucharest, Splaiul Independeneţei no. 313, Bucharest, Romania

Annals of West University of Timisoara - Mathematics and Computer Science

{"article-title":"A Kernel Based Clustering Algorithm using Particle Swarm Optimization"}

Unsupervised learning is one of the major research areas in machine learning, while kernel methods provide eficient solutions for various statistical learning...

A Kernel Based Clustering Algorithm using Particle Swarm Optimization

Pubblicato online: 14 ago 2013

Pagine: 91 - 108

DOI: https://doi.org/10.2478/awutm-2013-0007

Parole chiave
clustering algorithm, kernel function, Particle Swarm Optimization, discriminant function, emergent behaviour

This content is open access.

A Kernel Based Clustering Algorithm using Particle Swarm Optimization

Pubblicato online: 14 ago 2013

Pagine: 91 - 108

DOI: https://doi.org/10.2478/awutm-2013-0007

Parole chiaveclustering algorithm, kernel function, Particle Swarm Optimization, discriminant function, emergent behaviour

This content is open access.

Parole chiave
clustering algorithm, kernel function, Particle Swarm Optimization, discriminant function, emergent behaviour