MoCham: Robust Hierarchical Clustering Based on Multi-objective Optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F16%3A00307016" target="_blank" >RIV/68407700:21240/16:00307016 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/document/7836754/" target="_blank" >http://ieeexplore.ieee.org/document/7836754/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDMW.2016.0123" target="_blank" >10.1109/ICDMW.2016.0123</a>

Result language
angličtina
Original language name
MoCham: Robust Hierarchical Clustering Based on Multi-objective Optimization
Original language description
Many clustering evaluation methods are computed as a ratio between two objectives, typically these objectives express the compactness of all clusters while trying to maximize the separation between individual clusters. However, the clustering process itself is typically implemented as a single objective problem: optimizing a linear combination of between-points closeness. We propose MoCham - a hierarchical clustering algorithm that uses a multi-objective optimization for finding the optimal data points to merge. Our results suggest that a careful candidate selection of Pareto dominating pairs leads to more robust clustering results.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2016 IEEE 16th International Conference on Data Mining Workshops
ISBN
978-1-5090-5910-2
ISSN
2375-9259
e-ISSN
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Number of pages
8
Pages from-to
831-838
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Barcelona
Event date
Dec 12, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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