Clustering Based Classification in Data Mining Method Recommendation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10194486" target="_blank" >RIV/00216208:11320/13:10194486 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/13:00425703
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Clustering Based Classification in Data Mining Method Recommendation
Original language description
With the growing amount of data available in today's world, the emphasis is laid on the automatic configuration of data analysis - metalearning. This paper elaborates one of the metalearning subproblems, the data mining method recommendation. Based on ametric over the data features called metadata, we have proposed a solution exploiting clustering of datasets. The agglomerative algorithm is used to construct clustering over the metadata, and the average methods' performance is computed in each cluster.The ranking of data mining methods is then deduced from the classification of a dataset to a particular cluster. The recommendation algorithm, which is implemented within our data mining multi-agent system, has been tested in various configurations, andthe results of these experiments have been compared.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
—
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
12th International Conference on Machine Learning and Applications
ISBN
978-0-7695-5144-9
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
356-361
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos, USA
Event location
Miami, Florida, USA
Event date
Dec 4, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—