DocumentCode
501380
Title
Notice of Retraction
Fuzzy Clustering Based on Quotient Space and Its Application in CRM
Author
Lu Bin ; Zhao Xiaomin ; Jin Ranran
Author_Institution
Sch. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding, China
Volume
1
fYear
2009
fDate
15-17 May 2009
Firstpage
495
Lastpage
498
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.
By using method of fuzzy mathematics to quantitatively determine fuzzy relationship between samples, the fuzzy clustering analysis is able to reflect the world objectively and accurately. In this paper, combing the ideas of quotient space theory, hierarchical structure and fuzzy synthetic evaluation, a new model of fuzzy clustering analysis is proposed based on quotient space, which uses theory of granularities for attributes naturalization. The method not only reduces dimensions of attributes but also takes into account all effects of the important attributes by mapping them from low levels to high levels. Additionally, a new formula is proposed for similar matrix construction. In order to reduce the blindness for determination of the categories number, the threshold process is introduced, and we can gain the cluster outcome as soon as possible by regulating the grain-size. Finally, we did customer relationship management (CRM) in application and it is proved that the model is effective and reasonable for classification of multi-dimensional data.
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.
By using method of fuzzy mathematics to quantitatively determine fuzzy relationship between samples, the fuzzy clustering analysis is able to reflect the world objectively and accurately. In this paper, combing the ideas of quotient space theory, hierarchical structure and fuzzy synthetic evaluation, a new model of fuzzy clustering analysis is proposed based on quotient space, which uses theory of granularities for attributes naturalization. The method not only reduces dimensions of attributes but also takes into account all effects of the important attributes by mapping them from low levels to high levels. Additionally, a new formula is proposed for similar matrix construction. In order to reduce the blindness for determination of the categories number, the threshold process is introduced, and we can gain the cluster outcome as soon as possible by regulating the grain-size. Finally, we did customer relationship management (CRM) in application and it is proved that the model is effective and reasonable for classification of multi-dimensional data.
Keywords
customer relationship management; fuzzy set theory; CRM; customer relationship management; fuzzy clustering analysis; fuzzy mathematics; fuzzy relationship; fuzzy synthetic evaluation; hierarchical structure; quotient space quotient space theory; Application software; Blindness; Computer science; Customer relationship management; Image analysis; Information analysis; Information technology; Mathematics; Multidimensional systems; Space technology; CRM; Fuzzy cluster analysis; granularity; quotient space;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location
Chengdu
Print_ISBN
978-0-7695-3600-2
Type
conf
DOI
10.1109/IFITA.2009.59
Filename
5231673
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