DocumentCode
3573254
Title
The self-organization by lateral inhibition model: validation of clustering
Author
Tang, Bin ; Heywood, Malcolm I. ; Shepherd, Michael
Author_Institution
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
Volume
1
fYear
2003
Firstpage
781
Abstract
An improved version of the self-organization by lateral inhibition model (SOLI) has been applied to two synthetic data sets as well as the breast cancer and liver data sets, two well-known benchmark data sets. The methodology developed combines the use of various validity indices with the developed combines with the use of various validity indices with the SOLI model to discover the proper cluster structure within the data sets. In addition, the results explain why the breast cancer data set trends to be clustered so accurately while the liver data set trends to be so difficult to cluster accurately.
Keywords
cancer; data mining; liver; pattern clustering; self-organising feature maps; breast cancer data set; cluster structure; clustering validation; liver data sets; self-organization lateral inhibition model; synthetic data sets; Breast cancer; Clustering algorithms; Computational intelligence; Computer science; Electronic mail; Liver; Merging; Neural networks; Partitioning algorithms; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-7898-9
Type
conf
DOI
10.1109/IJCNN.2003.1223481
Filename
1223481
Link To Document