Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03136549" target="_blank" >RIV/68407700:21230/08:03136549 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors
Original language description
Sequential data represent an important source of potentially new medical knowledge. However, this type of data is rarely provided in a format suitable for immediate application of conventional mining algorithms. This paper summarizes and compares three different sequential mining approaches, based respectively on windowing, episode rules and inductive logic programming. Windowing is one of the essential methods of data preprocessing, episode rules represent general sequential mining while inductive logic programming extracts first order features whose structure is determined by background knowledge. The three approaches are demonstrated and evaluated in terms of a case study STULONG. It is a longitudinal preventive study of atherosclerosis where the data consist of series of longterm observations recording the development of risk factors and associated conditions.
Czech name
Dolování sekvenčních dat: srovnávací případová studie vývoje rizikových faktorů aterosklerózy
Czech description
Sekvenční data jsou významným zdrojem potenciálně nových lékařských znalostí. Článek aplikuje a srovnává tři různé přístupy sekvenčního dolování dat v případové studii vývoje rizikových faktorů aterosklerózy.
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/1ET101210513" target="_blank" >1ET101210513: Relational machine learning for analysis of biomedical data</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Systems, Man, and Cybernetics: Part C
ISSN
1094-6977
e-ISSN
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Volume of the periodical
38
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
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UT code for WoS article
000251840500002
EID of the result in the Scopus database
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