Partitional clustering algorithms for symbolic interval data based on single adaptive distances

DOI10.1016/j.patcog.2008.11.016zbMath1183.68527OpenAlexW2025818402MaRDI QIDQ1015177

Yves Lechevallier, Francisco de. A. T. de Carvalho

Publication date: 7 May 2009

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.11.016

zbMATH Keywords

symbolic data analysisadaptive distancescluster interpretation indicespartition interpretation indicespartitional clustering methodssymbolic interval data

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Pattern recognition, speech recognition (68T10)

Related Items (16)

Partition of interval-valued observations using regression ⋮ Outlier detection in interval data ⋮ Possibilistic Clustering Methods for Interval-Valued Data ⋮ Two clustering methods based on the Ward's method and dendrograms with interval-valued dissimilarities for interval-valued data ⋮ Batch self-organizing maps for distributional data with an automatic weighting of variables and components ⋮ Co-clustering algorithms for distributional data with automated variable weighting ⋮ Classification of the entities represented by samples from Gaussian distribution ⋮ Standardization of interval symbolic data based on the empirical descriptive statistics ⋮ Uncertainty measurement for interval-valued information systems ⋮ Self-organizing map for symbolic data ⋮ Exponential distance-based fuzzy clustering for interval-valued data ⋮ Fuzzy clustering of distributional data with automatic weighting of variable components ⋮ Far beyond the classical data models: symbolic data analysis ⋮ On clustering uncertain and structured data with Wasserstein barycenters and a geodesic criterion for the number of clusters ⋮ Clustering of interval time series ⋮ Association measures for interval variables

Cites Work

• New clustering methods for interval data
• Dynamic clustering for interval data based on \(L_2\) distance
• Multivalued type dissimilarity measure and concept of mutual dissimilarity value for clustering symbolic patterns
• On-line hierarchical clustering
• Clustering and its validation in a symbolic framework
• Generalized Minkowski metrics for mixed feature-type data analysis
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
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