Biclustering based on collinear patterns

Abstract

Data mining technique based on minimization of the convex and piecewise linear (CPL) criterion functions can be used to extract collinear (flat) patterns from large, multidimensional data sets. Flat patterns consist of data vectors located on planes in a multidimensional feature space. Data subsets located on such planes can represent linear interactions between multiple variables (features). New method of collinear biclustering can also be developed through this technique.