Combining PSO cluster and nonlinear mapping algorithm to perform clustering performance analysis: take the enterprise financial alarming as example

Combining PSO cluster and nonlinear mapping algorithm to perform clustering performance analysis:... Algorithm by simulating biological intelligence concept with application in the optimization issue is still in the emergent stage. Among them, Particle Swarm Optimization Algorithm is a concept and method that has group intelligence. Using the exploring and development feature of particle swarm, the best solution of the entire domain is searched in the question space. However, Clustering Analysis has an objective to plan the data set according to certain principle into several sub-sets. Due to the practicability of Clustering Analysis, many scholars propose different clustering algorithms to be used by the researchers. Therefore, in this article, Particle Swarm Optimization Algorithm is adopted to self-write Clustering Analysis program. Then Fuzzy Sammon Mapping nonlinear mapping algorithm is associated to perform clustering performance and classificaton capability assessment. From the test result of test data of enterprise’s financial alarming, it can be seen that Particle Swarm Clustering Analysis can obtain good clustering performance, and good classification capability can be obtained too. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Combining PSO cluster and nonlinear mapping algorithm to perform clustering performance analysis: take the enterprise financial alarming as example

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-011-9458-5
Publisher site
See Article on Publisher Site

Abstract

Algorithm by simulating biological intelligence concept with application in the optimization issue is still in the emergent stage. Among them, Particle Swarm Optimization Algorithm is a concept and method that has group intelligence. Using the exploring and development feature of particle swarm, the best solution of the entire domain is searched in the question space. However, Clustering Analysis has an objective to plan the data set according to certain principle into several sub-sets. Due to the practicability of Clustering Analysis, many scholars propose different clustering algorithms to be used by the researchers. Therefore, in this article, Particle Swarm Optimization Algorithm is adopted to self-write Clustering Analysis program. Then Fuzzy Sammon Mapping nonlinear mapping algorithm is associated to perform clustering performance and classificaton capability assessment. From the test result of test data of enterprise’s financial alarming, it can be seen that Particle Swarm Clustering Analysis can obtain good clustering performance, and good classification capability can be obtained too.

Journal

Quality & QuantitySpringer Journals

Published: Mar 4, 2011

References

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