SAKM: Self-adaptive kernel machine A kernel-based algorithm for online clustering

This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account varia...

Full description

Saved in:
Bibliographic Details
Published in:Neural networks 2008-11, Vol.21 (9), p.1287-1301
Main Authors: Amadou Boubacar, Habiboulaye, Lecoeuche, Stéphane, Maouche, Salah
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Cluster Analysis
Evolving models
Exact sciences and technology
Flows in networks. Combinatorial problems
Information, signal and communications theory
Models, Statistical
Multi-class problems
Non-stationary data
Online clustering
Online Systems
Operational research and scientific management
Operational research. Management science
Risk
RKHS
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Stochastic Processes
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2008.03.016