Initialization of Recursive Mixture-based Clustering with Uniform Components

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00476595" target="_blank" >RIV/67985556:_____/17:00476595 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0006417104490458" target="_blank" >http://dx.doi.org/10.5220/0006417104490458</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0006417104490458" target="_blank" >10.5220/0006417104490458</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Initialization of Recursive Mixture-based Clustering with Uniform Components

Popis výsledku v původním jazyce

The paper deals with a task of initialization of the recursive mixture estimation for the case of uniform components. This task is significant as a part of mixture-based clustering, where data clusters are described by the uniform distributions. The issue is extensively explored for normal components. However, sometimes the assumption of normality is not suitable or limits potential application areas (e.g., in the case of data with fixed bounds). The use of uniform components can be beneficial for these cases. Initialization is always a critical task of the mixture estimation. Within the considered recursive estimation algorithm the key point of its initialization is a choice of initial statistics of components. The paper explores several initialization approaches and compares results of clustering with a theoretical counterpart. Experiments with real data are demonstrated.

Název v anglickém jazyce

Initialization of Recursive Mixture-based Clustering with Uniform Components

Popis výsledku anglicky

The paper deals with a task of initialization of the recursive mixture estimation for the case of uniform components. This task is significant as a part of mixture-based clustering, where data clusters are described by the uniform distributions. The issue is extensively explored for normal components. However, sometimes the assumption of normality is not suitable or limits potential application areas (e.g., in the case of data with fixed bounds). The use of uniform components can be beneficial for these cases. Initialization is always a critical task of the mixture estimation. Within the considered recursive estimation algorithm the key point of its initialization is a choice of initial statistics of components. The paper explores several initialization approaches and compares results of clustering with a theoretical counterpart. Experiments with real data are demonstrated.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA15-03564S" target="_blank" >GA15-03564S: Shlukování a klasifikace s využitím rekurzivního odhadování modelu směsi distribucí</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017)

ISBN

978-989-758-263-9

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

449-458

Název nakladatele

SCITEPRESS

Místo vydání

Setúbal

Místo konání akce

Madrid

Datum konání akce

26. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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