An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Self-Organizing Maps
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
DS '00 Proceedings of the Third International Conference on Discovery Science
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Soft topographic map for clustering and classification of bacteria
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
IEEE Transactions on Information Theory
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Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a long procedure and the obtained dissimilarity results is not a metric. Recently the normalized compression distance was introduced as a method to calculate the distance between two generic digital objects, and it seems a suitable way to compare genomic strings. In this paper the clustering and the mapping, obtained using a SOM, with the traditional evolutionary distance and the compression distance are compared in order to understand if the two distances sets are similar. The first results indicate that the two distances catch different aspects of the genomic sequences and further investigations are needed to obtain a definitive result.