Fuzzy \(K\)-means clustering algorithms for interval-valued data based on adaptive quadratic distances

DOI10.1016/j.fss.2010.08.003zbMath1204.62106OpenAlexW2040579227MaRDI QIDQ622065

Camilo P. Tenório, Francisco de. A. T. de Carvalho

Publication date: 31 January 2011

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2010.08.003

zbMATH Keywords

data analysisfuzzy clusteringfuzzy statisticsinterval-valued datasymbolic data analysisadaptive quadratic distancesfuzzy cluster interpretation indexes

Mathematics Subject Classification ID

Related Items (14)

Possibilistic Clustering Methods for Interval-Valued Data ⋮ Similarity measures of interval-valued fuzzy sets ⋮ Fuzzy clustering of mixed data ⋮ Two clustering methods based on the Ward's method and dendrograms with interval-valued dissimilarities for interval-valued data ⋮ Trimmed fuzzy clustering for interval-valued data ⋮ Fuzzy \(c\)-ordered-means clustering ⋮ Participatory Learning Fuzzy Clustering for Interval-Valued Data ⋮ A fuzzy clustering approach for fuzzy data based on a generalized distance ⋮ Self-organizing map for symbolic data ⋮ A hybrid fuzzy K-harmonic means clustering algorithm ⋮ Exponential distance-based fuzzy clustering for interval-valued data ⋮ Far beyond the classical data models: symbolic data analysis ⋮ Parameter estimation from interval-valued data using the expectation-maximization algorithm ⋮ Wavelet-based fuzzy clustering of interval time series

Uses Software

• SODAS

Cites Work

• Partitional fuzzy clustering methods based on adaptive quadratic distances
• New clustering methods for interval data
• Dynamic clustering for interval data based on \(L_2\) distance
• Fuzzy clustering algorithms for mixed feature variables.
• Symbolic Data Analysis
• Clustering and its validation in a symbolic framework
• Symbolic Data Analysis and the SODAS Software
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
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