DocumentCode
553936
Title
Notice of Retraction
Hybrid adaptive niche to improve particle swarm optimization clustering algorithm
Author
Lei Jiang ; Lixin Ding ; Yunwen Lei ; Ming Chen ; Zhigao Zeng
Author_Institution
State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
134
Lastpage
138
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.
Keywords
convergence; data analysis; data mining; particle swarm optimisation; pattern clustering; Merwe function; PSO clustering; data analysis; data mining technique; fitness optimization function; hybrid adaptive niche; particle swarm optimization clustering algorithm; Algorithm design and analysis; Clustering algorithms; Educational institutions; Indexes; Optimization; Particle swarm optimization; Partitioning algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6021907
Filename
6021907
Link To Document