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Pattern Recognition Techniques applied to Evaluation Engineering Problem


Maria Teresinha Arns Steiner, Anselmo Chaves Neto, S?lvia Neide Br?ulio, Valdir Alves


Vol. 9  No. 11  pp. 190-198


The purpose of this paper is to present a Pattern Recognition methodology composed by Multivariate Statistical Analysis techniques, in order to build a Multiple Linear Regression statistical model to evaluate real estates according to their characteristics (variables, attributes). First, a Clustering Analysis was applied to the data of each urban estate class (apartments, houses or plots) to obtain homogeneous clusters within each class. Next, the Principal Components Analysis (P.C.A.) was applied to solve the multicollinearity problem that may exist among the variables in the model. The scores of the principal components are then the new independent variables and with them, the Multiple Linear Regression model was adjusted for each cluster of similar estates, within each class. This methodology was applied to estates in the city of Campo Mour?o, Paran?, Brazil. The model for each similar cluster within each class of evaluated estates presented an adequate adjustment to the data and a satisfactory predictive capacity.


Evaluation Engineering, Clustering, Principal Components Analysis, Multiple Linear Regression