Highly Robust Methods in Data Mining

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00389648" target="_blank" >RIV/67985807:_____/13:00389648 - 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

Highly Robust Methods in Data Mining

Popis výsledku v původním jazyce

This paper is devoted to highly robust methods for information extraction from data, with a special attention paid to methods suitable for management applications. The sensitivity of available data mining methods to the presence of outlying measurementsin the observed data is discussed as a major drawback of available data mining methods. The paper proposes several newhighly robust methods for data mining, which are based on the idea of implicit weighting of individual data values. Particularly it propose a novel robust method of hierarchical cluster analysis, which is a popular data mining method of unsupervised learning. Further, a robust method for estimating parameters in the logistic regression was proposed. This idea is extended to a robust multinomial logistic classification analysis. Finally, the sensitivity of neural networks to the presence of noise and outlying measurements in the data was discussed. The method for robust training of neural networks for the task of function

Název v anglickém jazyce

Highly Robust Methods in Data Mining

Popis výsledku anglicky

This paper is devoted to highly robust methods for information extraction from data, with a special attention paid to methods suitable for management applications. The sensitivity of available data mining methods to the presence of outlying measurementsin the observed data is discussed as a major drawback of available data mining methods. The paper proposes several newhighly robust methods for data mining, which are based on the idea of implicit weighting of individual data values. Particularly it propose a novel robust method of hierarchical cluster analysis, which is a popular data mining method of unsupervised learning. Further, a robust method for estimating parameters in the logistic regression was proposed. This idea is extended to a robust multinomial logistic classification analysis. Finally, the sensitivity of neural networks to the presence of noise and outlying measurements in the data was discussed. The method for robust training of neural networks for the task of function

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Serbian Journal of Management

ISSN

1452-4864

e-ISSN

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Svazek periodika

8

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

16

Strana od-do

9-24

Kód UT WoS článku

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EID výsledku v databázi Scopus

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