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Ontological data mining. / Vityaev, Evgenii; Kovalerchuk, Boris.

в: Studies in Computational Intelligence, Том 683, 01.01.2017, стр. 277-292.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Vityaev, E & Kovalerchuk, B 2017, 'Ontological data mining', Studies in Computational Intelligence, Том. 683, стр. 277-292. https://doi.org/10.1007/978-3-319-51052-1_17

APA

Vityaev, E., & Kovalerchuk, B. (2017). Ontological data mining. Studies in Computational Intelligence, 683, 277-292. https://doi.org/10.1007/978-3-319-51052-1_17

Vancouver

Vityaev E, Kovalerchuk B. Ontological data mining. Studies in Computational Intelligence. 2017 янв. 1;683:277-292. doi: 10.1007/978-3-319-51052-1_17

Author

Vityaev, Evgenii ; Kovalerchuk, Boris. / Ontological data mining. в: Studies in Computational Intelligence. 2017 ; Том 683. стр. 277-292.

BibTeX

@article{63b4c7352b324b6eb82234ea8fb0b473,
title = "Ontological data mining",
abstract = "We propose the ontological approach to Data Mining that is based on: (1) the analysis of subject domain ontology, (2) information in data that are interpretable in terms of ontology, and (3) interpretability of Data Mining methods and their results in ontology. Respectively concepts of Data Ontology and Data Mining Method Ontology are introduced. These concepts lead us to a new Data Mining approach—Ontological Data Mining (ODM). ODM uses the information extracted from data which is interpretable in the subject domain ontology instead of raw data. Next we present the theoretical and practical advantages of this approach and the Discovery system that implements this approach. The value ofODMis demonstrated by solutions of the tasks from the areas of financial forecasting, bioinformatics and medicine.",
author = "Evgenii Vityaev and Boris Kovalerchuk",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-51052-1_17",
language = "English",
volume = "683",
pages = "277--292",
journal = "Studies in Computational Intelligence",
issn = "1860-949X",
publisher = "Springer-Verlag GmbH and Co. KG",

}

RIS

TY - JOUR

T1 - Ontological data mining

AU - Vityaev, Evgenii

AU - Kovalerchuk, Boris

PY - 2017/1/1

Y1 - 2017/1/1

N2 - We propose the ontological approach to Data Mining that is based on: (1) the analysis of subject domain ontology, (2) information in data that are interpretable in terms of ontology, and (3) interpretability of Data Mining methods and their results in ontology. Respectively concepts of Data Ontology and Data Mining Method Ontology are introduced. These concepts lead us to a new Data Mining approach—Ontological Data Mining (ODM). ODM uses the information extracted from data which is interpretable in the subject domain ontology instead of raw data. Next we present the theoretical and practical advantages of this approach and the Discovery system that implements this approach. The value ofODMis demonstrated by solutions of the tasks from the areas of financial forecasting, bioinformatics and medicine.

AB - We propose the ontological approach to Data Mining that is based on: (1) the analysis of subject domain ontology, (2) information in data that are interpretable in terms of ontology, and (3) interpretability of Data Mining methods and their results in ontology. Respectively concepts of Data Ontology and Data Mining Method Ontology are introduced. These concepts lead us to a new Data Mining approach—Ontological Data Mining (ODM). ODM uses the information extracted from data which is interpretable in the subject domain ontology instead of raw data. Next we present the theoretical and practical advantages of this approach and the Discovery system that implements this approach. The value ofODMis demonstrated by solutions of the tasks from the areas of financial forecasting, bioinformatics and medicine.

UR - http://www.scopus.com/inward/record.url?scp=85012077549&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-51052-1_17

DO - 10.1007/978-3-319-51052-1_17

M3 - Article

AN - SCOPUS:85012077549

VL - 683

SP - 277

EP - 292

JO - Studies in Computational Intelligence

JF - Studies in Computational Intelligence

SN - 1860-949X

ER -

ID: 10311748