VISRED: numerical data mining with linear and nonlinear techniques

Authors:
Antonio Dourado;Edgar Ferreira;Paulo Barbeiro
Affiliations:
Centro de Informática e Sistemas, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal;Centro de Informática e Sistemas, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal;Centro de Informática e Sistemas, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
Venue:
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Year:
2007

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Abstract

Numerical data mining is a task for which several techniques have been developed that can provide a quick insight into a practical problem, if an easy to use common software platform is available. VISRED- Data Visualisation by Space Reduction presented here, aims to be such a tool for data classification and clustering. It allows the quick application of Principal Component Analysis, Nonlinear Principal Component Analysis, Multi-dimensional Scaling (classical and non classical). For clustering several techniques have been included: hierarchical, k-means, subtractive, fuzzy kmeans, SOM- Self Organizing Map (batch and recursive versions). It reads from and writes to Excel sheets. Its utility is shown with two applications: the visbreaker process part of an oil refinery and the UCI benchmark problem of breast cancer diagnosis.