A Mountain Clustering Based on Improved PSO Algorithm

In order to find most centre of the density of the sample set this paper combines MCA and PSO, and presents a mountain clustering based on improved PSO (MCBIPSO) algorithm. A mountain clustering method constructs a mountain function according to the density of the sample, but it is not easy to find...

Full description

Saved in:
Bibliographic Details
Published inAdvances in Natural Computation pp. 477 - 481
Main Authors Shen, Hong-yuan, Peng, Xiao-qi, Wang, Jun-nian, Hu, Zhi-kun
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial Immune System
Artificial intelligence
Cluster Center
Computer science; control theory; systems
Exact sciences and technology
Immune Algorithm
Improve Particle Swarm Optimization
Particle Swarm Optimization Algorithm
Cluster analysis
Decidability
Mountain
Evolutionary algorithm
Classification
Density function
Neural network
Online AccessGet full text

Cover

More Information
Summary:In order to find most centre of the density of the sample set this paper combines MCA and PSO, and presents a mountain clustering based on improved PSO (MCBIPSO) algorithm. A mountain clustering method constructs a mountain function according to the density of the sample, but it is not easy to find all peaks of the mountain function. The improved PSO algorithm is used to find all peaks of the mountain function. The simulation results show that the MCBIPSO algorithm is successful in deciding the density clustering centers of data samples.
Bibliography:This research was supported by National Nature Science Foundation of China (50374079).
ISBN:9783540283201
354028320X
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539902_58