Fuzzy K-Minpen Clustering and K-nearest-minpen Classification Procedures Incorporating Generic Distance-Based Penalty Minimizers

DOI10.1007/978-3-319-40581-0_36zbMath1460.62103OpenAlexW2503644122MaRDI QIDQ5117225

Anna Cena, Marek Gagolewski

Publication date: 20 August 2020

Published in: Information Processing and Management of Uncertainty in Knowledge-Based Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-40581-0_36

zbMATH Keywords

classificationclusteringfuzzy \(k\)-means algorithmfusion functionspenalty minimizers (minpen)

Mathematics Subject Classification ID

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

Related Items

Penalty-based aggregation of multidimensional data

Uses Software

• flexclust
• ElemStatLearn

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Trapezoidal approximation and aggregation
• OM3: ordered maxitive, minitive, and modular aggregation operators -- axiomatic and probabilistic properties in an arity-monotonic setting
• Aggregation functions based on penalties
• A toolbox for \(K\)-centroids cluster analysis
• Metrics and orders in space of fuzzy numbers
• An efficient approach for the rank aggregation problem
• On Central Tendency and Dispersion Measures for Intervals and Hypercubes
• Indexing methods for approximate dictionary searching
• Ordering by weighted number of wins gives a good ranking for weighted tournaments
• The elements of statistical learning. Data mining, inference, and prediction