Abstract
Data mining algorithms can be used for discovering collinear patterns in data sets composed of a large number of multidimensional feature vectors. Collinear (flat) pattern is observed in data sets when many feature vectors are located on a plane in a feature space. Models of linear interactions between multiple features (genes) can be designed based on collinear patterns. Minimization of the convex and piecewise linear (CPL) criterion functions allows for efficient discovering of flat patterns even in cases of large data sets.
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Acknowledgments
The presented study was supported by the grant S/WI/2/2019 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.
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Bobrowski, L. (2019). Local Models of Interaction on Collinear Patterns. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_21
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DOI: https://doi.org/10.1007/978-3-030-28377-3_21
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