Local Models of Interaction on Collinear Patterns
AbstractData 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.
|Publication size in sheets||0.55|
|Book||Nguyen Ngoc Thanh, Chbeir Richard, Exposito Ernesto, Aniorté Philippe, Trawiński Bogdan (eds.): Computational Collective Intelligence, Lecture Notes in Artificial Intelligence, vol. 11683, 2019, Springer, ISBN 978-3-030-28376-6, [978-3-030-28377-3], 716 p., DOI:10.1007/978-3-030-28377-3|
|Keywords in English||Data mining Collinear patterns Linear interactions models CPL criterion functions|
|Internal identifier||ROC 19-20|
|Score||= 20.0, 13-01-2020, ChapterFromConference|
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