Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm

Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm

© 2020, Springer Nature Switzerland AG. Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions...

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Main Authors: Vladik Kreinovich, Olga Kosheleva, Shahnaz N. Shahbazova, Songsak Sriboonchitta
Format: Book Series
Published: 2020
Subjects:
Computer Science
Mathematics
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081610724&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68347
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Institution: Chiang Mai University
id th-cmuir.6653943832-68347
record_format dspace
spelling th-cmuir.6653943832-683472020-04-02T15:27:48Z Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm Vladik Kreinovich Olga Kosheleva Shahnaz N. Shahbazova Songsak Sriboonchitta Computer Science Mathematics © 2020, Springer Nature Switzerland AG. Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm’s empirical success. 2020-04-02T15:25:18Z 2020-04-02T15:25:18Z 2020-01-01 Book Series 18600808 14349922 2-s2.0-85081610724 10.1007/978-3-030-38893-5_4 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081610724&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/68347
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Mathematics
spellingShingle Computer Science
Mathematics
Vladik Kreinovich
Olga Kosheleva
Shahnaz N. Shahbazova
Songsak Sriboonchitta
Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
description © 2020, Springer Nature Switzerland AG. Recently, a new empirically successful algorithm was proposed for crisp clustering: the K-sets algorithm. In this paper, we show that a natural uncertainty-based formalization of what is clustering automatically leads to the mathematical ideas and definitions behind this algorithm. Thus, we provide an explanation for this algorithm’s empirical success.
format Book Series
author Vladik Kreinovich
Olga Kosheleva
Shahnaz N. Shahbazova
Songsak Sriboonchitta
author_facet Vladik Kreinovich
Olga Kosheleva
Shahnaz N. Shahbazova
Songsak Sriboonchitta
author_sort Vladik Kreinovich
title Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
title_short Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
title_full Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
title_fullStr Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
title_full_unstemmed Probabilistic and More General Uncertainty-Based (e.g., Fuzzy) Approaches to Crisp Clustering Explain the Empirical Success of the K-Sets Algorithm
title_sort probabilistic and more general uncertainty-based (e.g., fuzzy) approaches to crisp clustering explain the empirical success of the k-sets algorithm
publishDate 2020
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081610724&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68347
_version_ 1681426803603800064

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