Extraction of Classification Rules from Sequences of Crystal Growth Data

0533904 - ÚI 2021 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Buša, R. - Dauxais, Y. - Ecklebe, S. - Dropka, N. - Holeňa, Martin
Extraction of Classification Rules from Sequences of Crystal Growth Data.
Proceedings of the 20th Conference Information Technologies - Applications and Theory. Aachen: Technical University & CreateSpace Independent Publishing, 2020 - (Holeňa, M.; Horváth, T.; Kelemenová, A.; Mráz, F.; Pardubská, D.; Plátek, M.; Sosík, P.), s. 101-107. CEUR Workshop Proceedings, 2718. ISSN 1613-0073.
[ITAT 2020: Information Technologies - Applications and Theory /20./. Oravská Lesná (SK), 18.09.2020-22.09.2020]
Grant CEP: GA ČR(CZ) GA18-18080S
Institucionální podpora: RVO:67985807
Klíčová slova: Classification Rules * Sequence analysis * Discriminant chronicles * Chronicles mining * Crystal growth data
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2718/paper02.pdf

The paper presents a generalization of a data mining method for the extraction of classification rules for classification of sequences of events, which is called discriminant chronicles mining. The generalization is motivated by the objective to extract classification rules from crystal growth data, for which the original method needs to be extended to events with vectors of attributes and to real-valued attributes. The paper elaborates incorporating both extensions into the theoretical fundamentals of the original method, and describes a corresponding modification of a system for discriminant chronicles mining, which has been developed three years ago to implement the original method. Finally, an application of the generalized method, using the modified system for discriminant chronicles mining, to data from the growth of GaAs crystals by vertical gradient freeze method is briefly sketched.

Trvalý link: http://hdl.handle.net/11104/0312134