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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03136549%21RIV09-MSM-21230___
rdf:type
skos:Concept n16:Vysledek
dcterms: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. 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. 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.
dcterms:title
Dolování sekvenčních dat: srovnávací případová studie vývoje rizikových faktorů aterosklerózy Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors
skos:prefLabel
Dolování sekvenčních dat: srovnávací případová studie vývoje rizikových faktorů aterosklerózy Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors
skos:notation
RIV/68407700:21230/08:03136549!RIV09-MSM-21230___
n3:aktivita
n11:P n11:Z
n3:aktivity
P(1ET101210513), Z(MSM6840770012)
n3:cisloPeriodika
1
n3:dodaniDat
n4:2009
n3:domaciTvurceVysledku
n5:5112605 n5:9942904 n5:5879523 n5:7879830 n5:5523036
n3:druhVysledku
n13:J
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
394466
n3:idVysledku
RIV/68407700:21230/08:03136549
n3:jazykVysledku
n7:eng
n3:klicovaSlova
anachronism; episode rules; inductive logic programming; temporal pattern; trend analysis; windowing
n3:klicoveSlovo
n6:trend%20analysis n6:temporal%20pattern n6:episode%20rules n6:inductive%20logic%20programming n6:windowing n6:anachronism
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[406CB7AB22CF]
n3:nazevZdroje
IEEE Transactions on Systems, Man, and Cybernetics: Part C
n3:obor
n17:JC
n3:pocetDomacichTvurcuVysledku
5
n3:pocetTvurcuVysledku
5
n3:projekt
n9:1ET101210513
n3:rokUplatneniVysledku
n4:2008
n3:svazekPeriodika
38
n3:tvurceVysledku
Štěpánková, Olga Železný, Filip Karel, Filip Nováková, Lenka Kléma, Jiří
n3:wos
000251840500002
n3:zamer
n18:MSM6840770012
s:issn
1094-6977
s:numberOfPages
13
n8:organizacniJednotka
21230