title: How to make large self-organizing maps for nonvectorial data.
abstract: The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.
authors.affiliation: Neural Networks Research Centre, Helsinki University of Technology, Finland. teuvo.kohonen@hut.fi
authors.forename: Teuvo
authors.initials: T
authors.lastname: Kohonen
authors.forename: Panu
authors.initials: P
authors.lastname: Somervuo
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D016571:Neural Networks, Computer; D020539:Sequence Analysis, Protein / Q000379:methods