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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F11%3A00203370%21RIV13-MSM-21230___
rdf:type
skos:Concept n18:Vysledek
rdfs:seeAlso
http://www.sciencedirect.com/science/article/pii/S1877050911006399
dcterms:description
In this work we have studied, evaluated and proposed different swarm intelligence techniques for mining information from loosely structured medical textual records with no a priori knowledge (a large dataset). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining. First, classical approaches such as basic statistic approaches, single and multiple word frequency analysis, etc., have been used to simplify the textual data and provide an overview of the data. Finally, an ant-inspired self-learning approach has been used to automatically provide a simplified dominant structure, presenting structure of the records in the human readable form that can be further utilized in the mining process as it describes the vast majority of the records. In this work we have studied, evaluated and proposed different swarm intelligence techniques for mining information from loosely structured medical textual records with no a priori knowledge (a large dataset). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining. First, classical approaches such as basic statistic approaches, single and multiple word frequency analysis, etc., have been used to simplify the textual data and provide an overview of the data. Finally, an ant-inspired self-learning approach has been used to automatically provide a simplified dominant structure, presenting structure of the records in the human readable form that can be further utilized in the mining process as it describes the vast majority of the records.
dcterms:title
Novel Nature Inspired Techniques in Medical Data Mining Novel Nature Inspired Techniques in Medical Data Mining
skos:prefLabel
Novel Nature Inspired Techniques in Medical Data Mining Novel Nature Inspired Techniques in Medical Data Mining
skos:notation
RIV/68407700:21230/11:00203370!RIV13-MSM-21230___
n18:predkladatel
n19:orjk%3A21230
n3:aktivita
n5:Z
n3:aktivity
Z(MSM6840770012)
n3:dodaniDat
n4:2013
n3:domaciTvurceVysledku
n16:3558568 n16:9431446
n3:druhVysledku
n20:O
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
216598
n3:idVysledku
RIV/68407700:21230/11:00203370
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Swarm Intelligence; Ant Colony; Text Mining; Data Mining; Medical Record Processing
n3:klicoveSlovo
n11:Medical%20Record%20Processing n11:Text%20Mining n11:Swarm%20Intelligence n11:Data%20Mining n11:Ant%20Colony
n3:kontrolniKodProRIV
[43B8B03CB49B]
n3:obor
n14:JC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n4:2011
n3:tvurceVysledku
Burša, Miroslav Lhotská, Lenka
n3:zamer
n9:MSM6840770012
n6:doi
10.1016/j.procs.2011.09.079
n8:organizacniJednotka
21230