Self-organizing maps
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Sequence - Evolution - Function: Computational Approaches in Comparative Genomics
Sequence - Evolution - Function: Computational Approaches in Comparative Genomics
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
IEEE Transactions on Information Theory
Soft topographic maps for clustering and classifying bacteria using housekeeping genes
Advances in Artificial Neural Systems
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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 time consuming procedure and the obtained dissimilarity results is not a metric. Recently, the normalised 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 non-linear mapping obtained using the evolutionary distance and the compression distance are compared, in order to understand if the two distances sets are similar.