Deterministic walks as an algorithm of pattern recognition.
New tools for automatically finding data clusters that share statistical properties in a heterogeneous data set are imperative in pattern recognition research. Here we introduce a deterministic procedure as a tool for pattern recognition in a hierarchical way. The algorithm finds attractors of mutually close points based on the neighborhood ranking. A memory parameter mu acts as a hierarchy parameter, in which the clusters are identified. The final result of the method is a general tree that represents the nesting structure of the data in an invariant way by scale transformation.
Campiteli
Mônica G
MG
Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Avenida Bandeirantes 3900, 14040-901 Ribeirão Preto, São Paulo, Brazil. monicacampiteli@pg.ffclrp.usp.br
Batista
Pablo D
PD
Kinouchi
Osame
O
Martinez
Alexandre S
AS