Data Mining and Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F17%3A%230000225" target="_blank" >RIV/04274644:_____/17:#0000225 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31140/17:00050058
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Data Mining and Machine Learning
Original language description
The rapid growth of data collected and stored in various application areas brings new problems and challenges in their processing and interpretation. While database technology provides tools for data storage and “simple” querying, and statistics offers methods for analyzing small sample data, new approaches are necessary to face these challenges. These approaches are usually called knowledge discovery in databases (KDD) or data mining. KDD can be applied in various domains: banking and finance, insurance, life sciences, retail, technical diagnostics, computer networks, social networks e.t.c. Let us consider an example from medical domain, the analysis of atherosclerosis risk factors data with the aim to build a model that will differentiate between normal and risky patients.
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal on Biomedicine and Healthcare
ISSN
1805-8698
e-ISSN
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Volume of the periodical
5
Issue of the periodical within the volume
1
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
2
Pages from-to
53-54
UT code for WoS article
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EID of the result in the Scopus database
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