Decision Rules with Collinearity Models
Data mining algorithms are used for discovering general regularities based on the observed patterns in data sets. Flat (multicollinear) patterns can be observed in data sets when many feature vectors are located on a planes in the multidimensional feature space. Collinear patterns can be useful in modeling linear interactions between multiple variables (features) and can be used also in a decision support process. Flat patterns can be efficiently discovered in large, multivariate data sets through minimization of the convex and piecewise linear (CPL) criterion functions.
KeywordsData mining Decision rules Collinear models CPL criterion functions
This work was supported by the project S/WI/2/2016 from the Białystok University of Technology, Poland.
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