Gaussian Mixture Model Cluster Forest
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86096026" target="_blank" >RIV/61989100:27240/16:86096026 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/16:86096026
Result on the web
<a href="http://ieeexplore.ieee.org/document/7424454/" target="_blank" >http://ieeexplore.ieee.org/document/7424454/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICMLA.2015.12" target="_blank" >10.1109/ICMLA.2015.12</a>
Alternative languages
Result language
angličtina
Original language name
Gaussian Mixture Model Cluster Forest
Original language description
Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms. This article describes a new ensemble clustering algorithm based on CF that internally uses a probabilistic model called Gaussian Mixture Model (GMM). Finally, Expectation-maximization algorithm is used for estimation of GMM parameters. The proposed ensemble clustering algorithm will be compared with several different approaches and tested on eight datasets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
ISBN
978-1-5090-0287-0
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1019-1023
Publisher name
IEEE
Place of publication
Vienna
Event location
Miami
Event date
Dec 9, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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