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
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;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223481
