A new technique Gray scale display of input data using shooting SOM and genetic algorithm

Abstract We present the Self-Organizing Map (SOM) is popular algorithm for unsupervised learning, which is widely applied for many applications. In the result, we have proposed a new type of SOM algorithm, The important feature of SSOM is that the neurons move like aiming at a target, namely and only some neurons near the cluster move toward the cluster to hit the area where input data are concentrated and 1 cell to neighborhood neurons cell of the winner neuron get away a fraction of an inch from the cluster. The feature, SSOM tends to self-organize each cluster along the figure of each cluster. We investigate the behavior of SSOM and apply Genetic Algorithm to data visualization problems.

[1]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[2]  F. Saitoh.  Image contrast enhancement using genetic algorithm , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[3]  Agostinho C. Rosa,et al.  Towards automatic image enhancement using genetic algorithms , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[6]  Erkki Oja,et al.  PicSOM-self-organizing image retrieval with MPEG-7 content descriptors , 2002, IEEE Trans. Neural Networks.

[7]  Francesco Palmieri,et al.  Self-association in multilayer linear networks with limited connectivity , 1998, Neural Networks.

[8]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[9]  Günter Rudolph,et al.  Contemporary Evolution Strategies , 1995, ECAL.

[10]  Yoshifumi Nishio,et al.  Shooting SOM and its Application for Clustering , 2006 .

[11]  Chung-Chian Hsu,et al.  Generalizing self-organizing map for categorical data , 2006, IEEE Transactions on Neural Networks.