Local Models of Interaction on Collinear Patterns

Leon Bobrowski

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.
Author Leon Bobrowski (FCS / SD)
Leon Bobrowski,,
- Software Department
Pages259-270
Publication size in sheets0.55
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 EnglishData mining Collinear patterns Linear interactions models CPL criterion functions
DOIDOI:10.1007/978-3-030-28377-3_21
Internal identifierROC 19-20
Languageen angielski
Score (nominal)20
Score sourceconferenceList
ScoreMinisterial score = 20.0, 13-01-2020, ChapterFromConference
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