ACODF: a novel data clustering approach for data mining in large databases
Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
| Hi-index | 0.00 |
The clustering method by the self-organizing map algorithm of chromosome profiles measured by slit-scan flow-cytometer is proposed. Moreover, the physical models of chromosomes have been introduced in order to take into account the rotation of chromosomes in the flow-cytometer. By this modification, the lengths of chromosomes and the intensity distribution of chromosome fluorescence can be estimated from chromosome profile data measured by the flow-cytometer. However, the clustering results did not converged identically in some experiments and the distribution of the rotation angles was unnatural. Therefore, we introduced simulated annealing to improve the convergence of our SOM algorithm. We compared the clustering results of this method with those of K-means method and SOM method.