Análise de agrupamentos baseada na topologia dos dados e em mapas auto-organizáveis. / Data clustering based on data topology and self organizing-maps.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

More than ever, in environment of large decision making, the analysis of data stored massively becomes a real need in almost all knowledge areas. The data analyzing process covers the performing of different tasks that can be executed for different techniques and strategies as the data clustering analysis. This research is focused on the analysis task of data groups, called Data Clustering using Self Organizing Maps (SOM) as principal artifact. SOM is an artificial neural network based on competitive and unsupervised learning, what means that its training is entirely driven by the data, such the neurons of the map compete themselves for doing it. This neural network has the ability to build the mapping task that quantifies the source data, but preserving the topology. This work introduces a new clustering analysis methodology based on SOM, considering the topological map produced by it and also the topology of the data obtained in the clustering process. The experimental and comparative analysis are also presented to demonstrate the potential of the proposal, highlighting at the end the mainly contributions of the work.

ASSUNTO(S)

análise de agrupamentos self-organizing maps (som) knowledge discovery análise exploratória de dados data clustering descoberta de conhecimento mineração de dados mapas auto-organizáveis (som) data mining exploratory data analysis

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