Possibilistic Clustering Methods for Interval-Valued Data

DOI10.1142/S0218488514500135zbMath1323.62055MaRDI QIDQ3449300

Renata M. C. R. de Souza, Bruno Almeida Pimentel

Publication date: 4 November 2015

Published in: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (Search for Journal in Brave)

zbMATH Keywords

noiseoutlierinterval datasymbolic data analysispossibilistic \(c\)-means clustering method

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Multivariate analysis and fuzziness (62H86)

Related Items (1)

Data generation processes and statistical management of interval data

Cites Work

• Fuzzy \(K\)-means clustering algorithms for interval-valued data based on adaptive quadratic distances
• Dynamic clustering for interval data based on \(L_2\) distance
• A robust fuzzy \(k\)-means clustering model for interval valued data
• Partitional clustering algorithms for symbolic interval data based on single adaptive distances
• Multivalued type dissimilarity measure and concept of mutual dissimilarity value for clustering symbolic patterns
• Dependencies and Variation Components of Symbolic Interval-Valued Data
• 6. Symbolic Data Analysis

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