FINDCLUS: fuzzy individual differences clustering

DOI10.1007/s00357-012-9109-0zbMath1360.62331OpenAlexW2099347259MaRDI QIDQ263346

Henk A. L. Kiers, Paolo E. Giordani

Publication date: 4 April 2016

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-012-9109-0

zbMATH Keywords

clusteringfuzzy approachINDCLUSproximity datathree-way analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20) Applications of statistics to psychology (62P15) Multivariate analysis and fuzziness (62H86)

Related Items (2)

ROOTCLUS: searching for ``ROOT clusters in three-way proximity data ⋮ GINDCLUS: generalized INDCLUS with external information

Uses Software

• FINDCLUS
• MAPCLUS
• Indclus

Cites Work

• Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features
• A fuzzy clustering model for multivariate spatial time series
• CLUSCALE (``Clustering and multidimensional SCAL[Eing): a three-way hybrid model incorporating overlapping clustering and multidimensional scaling structure]
• Structural classification analysis of three-way dissimilarity data
• Optimization strategies for two-mode partitioning
• A comparison of two methods for fitting the INDCLUS model
• Approximating a similarity matrix by a latent class model: a reappraisal of additive fuzzy clustering
• MAPCLUS: A mathematical programming approach to fitting the ADCLUS model
• A least squares algorithm for a mixture model for compositional data.
• An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models
• Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima
• A modification of the SINDCLUS algorithm for finding the ADCLUS and INCLUS models
• Fuzzy clustering models and applications
• Simultaneous component and clustering models for three-way data: Within and between approaches
• Fuzzy partition models for fitting a set of partitions
• Mathematical classification and clustering
• Archetypal Analysis
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