Abstract
Abstract
Today, technology is developing rapidly, and multidisciplinary field of science and
technology has become a common feature of many disciplines of the interdisciplinary
interaction penetration. The combination of intelligent computing information science
and life science is a kind of new interdisciplinary.
All swarm intelligence algorithms
already exists in the field of intelligent computing, are originated in the scientist
simulation. What’s more, analysis of the behavior of groups of organisms in nature
swarm intelligence algorithms (swarm intelligence algorithm), such algorithms in recent
years become a hot research field which as a typical representative of the swarm
intelligence algorithms. PSO (particle swarm optimization PSO) algorithm, due to it is
easy to implement, efficient attention has been paid, and the quantum particle swarm
algorithm Sun Jun et al (quantum-behaved particle swarm optimization, QPSO) not
only has the advantage of traditional particle swarm algorithm but overcome the
shortcomings of its non-global convergence as well. The concept of clustering ,which
has come into people's lives, clustering methods to solve practical problems and
services, over and over the years, clustering has become a focus of the field of data
mining, with the development of society, however, the actual of the problem is more
complex, and the past that need to pre-determine the number of categories algorithm
already can not meet the practical problems of modern complex, and automatic data
clustering cause academic attention in recent years, become the hotspot of the research
today. Kernel method is also a method of pattern recognition direction. Recent years,
Kernel methods are applied in many aspects, such as support vector product, Gaussian
process, which is determined by the characteristics of the low-dimensional to high
dimensional changes Kernel who makes the original clustering method even more
powerful in the field of data clustering. In this thesis the three proposed nuclear
clustering methods based on quantum particle swarm algorithm are not only a good
solution to the spherical automatic data clustering problem but non-spherical data
automatic clustering as well.
This thesis is organized as follows:
1) Based the QPSO algorithm automatically clustering algorithm, the method
takes full advantage the QPSO global convergence of the algorithm,
overcomes the premature convergence characteristics to accurately find
