Fuzzy Clustering High-Dimensional Data Using Information Weighting

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F19%3AA2001WKF" target="_blank" >RIV/61988987:17610/19:A2001WKF - isvavai.cz</a>
Result on the web
<a href="https://www.springer.com/gp/book/9783030209117" target="_blank" >https://www.springer.com/gp/book/9783030209117</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-20912-4_36" target="_blank" >10.1007/978-3-030-20912-4_36</a>

Result language
angličtina
Original language name
Fuzzy Clustering High-Dimensional Data Using Information Weighting
Original language description
The fuzzy clustering algorithm for high-dimensional data is proposed in this paper. An objective function which is insensitive to the “concentration of norms” phenomenon is also introduced. We recommend using a weighted parameter in the objective function. The proposed fuzzy clustering algorithm is compared with FCM in the experimental part. Dependence of the clustering algorithm’s results on the weighted parameter changes has also been investigated and tested.
Czech name
—
Czech description
—

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Lecture Notes in Artificial Intelligence. Artificial Intelligence and Soft Computing
ISBN
978-3-030-20911-7
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
385-395
Publisher name
Springer International Publishing AG
Place of publication
Switzerland
Event location
Zakopane, Poland
Event date
Jun 16, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000485150200036

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