Highly Robust Methods in Data Mining

Result code in 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>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Highly Robust Methods in Data Mining
Original language description
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
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)