K-EVCLUS: Clustering large dissimilarity data in the belief function framework
© Springer International Publishing Switzerland 2016. In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimila...
Saved in:
Main Authors: | Kanjanatarakul O., Sriboonchitta S., Denoeux T. |
---|---|
Format: | Book Series |
Published: |
2017
|
Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84988629130&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42548 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Chiang Mai University |
Similar Items
-
K-EVCLUS: Clustering large dissimilarity data in the belief function framework
by: Orakanya Kanjanatarakul, et al.
Published: (2018) -
Evidential clustering of large dissimilarity data
by: Denœux T., et al.
Published: (2017) -
Evidential clustering of large dissimilarity data
by: Thierry Denœux, et al.
Published: (2018) -
Forecasting using belief functions: An application to marketing econometrics
by: Kanjanatarakul O., et al.
Published: (2014) -
Prediction of future observations using belief functions: A likelihood-based approach
by: Kanjanatarakul O., et al.
Published: (2017)