This HTML5 document contains 49 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n17http://localhost/temp/predkladatel/
n11http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n16http://linked.opendata.cz/ontology/domain/vavai/
n14http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
n7http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F04%3A03107391%21RIV%2F2005%2FMSM%2F212305%2FN/
skoshttp://www.w3.org/2004/02/skos/core#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n9http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n12http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n6http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F04%3A03107391%21RIV%2F2005%2FMSM%2F212305%2FN
rdf:type
skos:Concept n16:Vysledek
dcterms:description
Není k dispozici The paper concerns mining data lacking the uniform structure. The data are collected from a number of objects during repeated measurements, all of which are tagged by a corresponding time. No attribute-valued machine learning algorithm can be applied directly on such data since the number of measurements is not fixed but it varies. The available data have to be transformed and preprocessed in such a way that a uniform type of information is obtained about all the considered objects. This can be achieved, e.g., by aggregation. But this process can introduce anachronistic variables, i.e., variables containing information which cannot be available at the moment when a prediction is needed. The paper suggests and tests a method how to preprocess the considered type of data without falling into a trap of introducing anachronistic attributes. The method is illustrated on a case study based on STULONG data. The paper concerns mining data lacking the uniform structure. The data are collected from a number of objects during repeated measurements, all of which are tagged by a corresponding time. No attribute-valued machine learning algorithm can be applied directly on such data since the number of measurements is not fixed but it varies. The available data have to be transformed and preprocessed in such a way that a uniform type of information is obtained about all the considered objects. This can be achieved, e.g., by aggregation. But this process can introduce anachronistic variables, i.e., variables containing information which cannot be available at the moment when a prediction is needed. The paper suggests and tests a method how to preprocess the considered type of data without falling into a trap of introducing anachronistic attributes. The method is illustrated on a case study based on STULONG data.
dcterms:title
Anachronistic Attributes in Temporal Data: A Case Study Anachronistic Attributes in Temporal Data: A Case Study Není k dispozici
skos:prefLabel
Anachronistic Attributes in Temporal Data: A Case Study Anachronistic Attributes in Temporal Data: A Case Study Není k dispozici
skos:notation
RIV/68407700:21230/04:03107391!RIV/2005/MSM/212305/N
n4:strany
421 ; 434
n4:aktivita
n12:Z
n4:aktivity
Z(MSM 210000012)
n4:cisloPeriodika
5
n4:dodaniDat
n6:2005
n4:domaciTvurceVysledku
n11:5879523 n11:5112605 n11:9942904
n4:druhVysledku
n18:J
n4:duvernostUdaju
n15:S
n4:entitaPredkladatele
n7:predkladatel
n4:idSjednocenehoVysledku
554137
n4:idVysledku
RIV/68407700:21230/04:03107391
n4:jazykVysledku
n5:eng
n4:klicovaSlova
Data mining; anachronistic attribute; data preprocessing; sequential data; temporal pattern; trend analysis; windowing
n4:klicoveSlovo
n9:Data%20mining n9:sequential%20data n9:data%20preprocessing n9:anachronistic%20attribute n9:temporal%20pattern n9:trend%20analysis n9:windowing
n4:kodStatuVydavatele
CZ - Česká republika
n4:kontrolniKodProRIV
[5A49D9A2FFF3]
n4:nazevZdroje
Neural Network World
n4:obor
n13:JD
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n6:2004
n4:svazekPeriodika
14
n4:tvurceVysledku
Nováková, Lenka Kléma, Jiří Štěpánková, Olga
n4:zamer
n14:MSM%20210000012
s:issn
1210-0552
s:numberOfPages
14
n17:organizacniJednotka
21230