DocumentCode
553938
Title
Notice of Retraction
The clustering method study based on graph
Author
Lin Chunmei ; He Yue
Author_Institution
Dept. of Compute, Shaoxing Univ., Shaoxing, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
276
Lastpage
279
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.
This paper proposes an clustering method of genes. In this method, the clustering problem is transferred the partition problem of non-directional graph. Firstly, we preprocess the gene expression data and select sensitive genes according to the variance value, the selected genes are mapped to a high-dimensional space with kernel method, and then, a non-directional graph is constructed according to similarity measure, we use evolutionary method to cluster the genes in the feature space. To demonstrate the effectiveness of the proposed method, the method is tested on yeast cell cycle expression data set; the results suggest that this method is capable of clustering genes.
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.
This paper proposes an clustering method of genes. In this method, the clustering problem is transferred the partition problem of non-directional graph. Firstly, we preprocess the gene expression data and select sensitive genes according to the variance value, the selected genes are mapped to a high-dimensional space with kernel method, and then, a non-directional graph is constructed according to similarity measure, we use evolutionary method to cluster the genes in the feature space. To demonstrate the effectiveness of the proposed method, the method is tested on yeast cell cycle expression data set; the results suggest that this method is capable of clustering genes.
Keywords
biocomputing; cellular biophysics; evolutionary computation; genetics; graph theory; microorganisms; pattern clustering; clustering method; evolutionary method; feature space; gene expression data; nondirectional graph; sensitive gene; yeast cell cycle expression data set; Clustering algorithms; Clustering methods; Gene expression; Genetic algorithms; Kernel; Partitioning algorithms; clustering; evolutionary; gene expression data; non-directional graph; partition;
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.2011.6021910
Filename
6021910
Link To Document