A Bayesian analysis of self-organizing maps
Neural Computation
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A stochastic self-organizing map for proximity data
Neural Computation
Self-Organizing Maps
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
SEQOPTICS: A Protein Sequence Clustering Method
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
International Journal of Knowledge Engineering and Soft Data Paradigms
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
A Quick Assessment of Topology Preservation for SOM Structures
IEEE Transactions on Neural Networks
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The Self-Organizing Map (SOM) algorithm is widely used for building topographic maps of data represented in a vectorial space, but it does not operate with dissimilarity data. Soft Topographic Map (STM) algorithm is an extension of SOM to arbitrary distance measures, and it creates a map using a set of units, organized in a rectangular lattice, defining data neighbourhood relationships. In the last years, a new standard for identifying bacteria using genotypic information began to be developed. In this new approach, phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so-called "housekeeping genes." The goal of this work is to build a topographic representation of bacteria clusters, by means of self-organizing maps, starting from genotypic features regarding housekeeping genes.