Recognizing characteristic patterns in distorted data collections

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86088268" target="_blank" >RIV/61989100:27740/13:86088268 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Recognizing characteristic patterns in distorted data collections

Popis výsledku v původním jazyce

Many models and artificial intelligence methods work with the inputs in the form of time series. Generally, success of many of them strongly depends on ability to successfully manage input data, which often contains repeating similar episodes (patterns).If these patterns are recognized, they can be used for instance for indexing, prediction or compression. These operations can also be very useful for improving the already existing model performance and accuracy. Our effort is to provide a robust mechanism for retrieving these characteristic patterns from the collections that are subject of various distortions. The whole process of our pattern recognition consists of receiving the episodes, their clustering into the groups of similar episodes and deriving the representatives of each cluster. These representatives will be used for further indexing collections. This paper is focused on the last step of this process - receiving the representatives of concrete clusters using Dynamic Time W

Název v anglickém jazyce

Recognizing characteristic patterns in distorted data collections

Popis výsledku anglicky

Many models and artificial intelligence methods work with the inputs in the form of time series. Generally, success of many of them strongly depends on ability to successfully manage input data, which often contains repeating similar episodes (patterns).If these patterns are recognized, they can be used for instance for indexing, prediction or compression. These operations can also be very useful for improving the already existing model performance and accuracy. Our effort is to provide a robust mechanism for retrieving these characteristic patterns from the collections that are subject of various distortions. The whole process of our pattern recognition consists of receiving the episodes, their clustering into the groups of similar episodes and deriving the representatives of each cluster. These representatives will be used for further indexing collections. This paper is focused on the last step of this process - receiving the representatives of concrete clusters using Dynamic Time W

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

25th European Modeling and Simulation Symposium, EMSS 2013

ISBN

978-88-97999-22-5

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

238-243

Název nakladatele

DIME Universit? Di Genova

Místo vydání

Genova

Místo konání akce

Athény

Datum konání akce

25. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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