Matrix computations (3rd ed.)
Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Self-Organizing Maps
Neural Networks for Chemists; An Introduction
Neural Networks for Chemists; An Introduction
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
Context-Aware Visual Exploration of Molecular Datab
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In the past decade, the amount of data in biological field has become larger and larger; Bio-techniques for analysis of biological data have been developed and new tools have been introduced. Several computational methods are based on unsupervised neural network algorithms that are widely used for multiple purposes including clustering and visualization, i.e. the Self Organizing Maps (SOM). Unfortunately, even though this method is unsupervised, the performances in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. In this paper we present a new initialization technique based on a totally connected undirected graph, that report relations among some intersting features of data input. Result of experimental tests, where the proposed algorithm is compared to the original initialization techniques, shows that our technique assures faster learning and better performance in terms of quantization error.