Unsupervised algorithm for post-processing of roughly segmented categorical time series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86084589" target="_blank" >RIV/61989100:27240/12:86084589 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/12:86084589

Výsledek na webu

<a href="http://dx.doi.org/10.3182/20090210-3-CZ-4002.0031" target="_blank" >http://dx.doi.org/10.3182/20090210-3-CZ-4002.0031</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20090210-3-CZ-4002.0031" target="_blank" >10.3182/20090210-3-CZ-4002.0031</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised algorithm for post-processing of roughly segmented categorical time series

Popis výsledku v původním jazyce

Many types of existing collections often contain repeating sequences which could be called as patterns. If these patterns are recognized they can be for instance used in data compression or for prediction. Extraction of these patterns from data collections with components generated in equidistant time and in finite number of levels is now a trivial task. The problem arises for data collections that are subject to different types of distortions in all axes. This paper discusses possibilities of using theVoting Experts algorithm enhanced by the Dynamic Time Warping (DTW) method. This algorithm is used for searching characteristic patterns in collections that are subject to the previously mentioned distortions. By using the Voting Experts high precisioncuts (but with low level of recall) are first created in the collection. These cuts are then processed using the DTW method to increase resulting recall. This algorithm has better quality indicators than the original Voting Experts algori

Název v anglickém jazyce

Unsupervised algorithm for post-processing of roughly segmented categorical time series

Popis výsledku anglicky

Many types of existing collections often contain repeating sequences which could be called as patterns. If these patterns are recognized they can be for instance used in data compression or for prediction. Extraction of these patterns from data collections with components generated in equidistant time and in finite number of levels is now a trivial task. The problem arises for data collections that are subject to different types of distortions in all axes. This paper discusses possibilities of using theVoting Experts algorithm enhanced by the Dynamic Time Warping (DTW) method. This algorithm is used for searching characteristic patterns in collections that are subject to the previously mentioned distortions. By using the Voting Experts high precisioncuts (but with low level of recall) are first created in the collection. These cuts are then processed using the DTW method to increase resulting recall. This algorithm has better quality indicators than the original Voting Experts algori

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

DATESO 2012 : databases, texts, specifications, and objects : proceedings of the Dateso 2012 Workshop : April 18-20, 2012, Zernov, Rovensko pod Troskami

ISBN

978-80-7378-171-2

ISSN

—

e-ISSN

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Počet stran výsledku

12

Strana od-do

81-92

Název nakladatele

MATFYZPRESS

Místo vydání

Praha

Místo konání akce

Žernov, Semily

Datum konání akce

18. 4. 2012

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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