New clustering methods for interval data
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Publication:880906
DOI10.1007/s00180-006-0260-0zbMath1114.62069OpenAlexW2004925710MaRDI QIDQ880906
Yves Lechevallier, Rosanna Verde, Marie Chavent, Francisco de. A. T. de Carvalho
Publication date: 29 May 2007
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-006-0260-0
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to environmental and related topics (62P12)
Related Items (16)
Data generation processes and statistical management of interval data ⋮ Exploratory data analysis of interval-valued symbolic data with matrix visualization ⋮ A new robust model of one-class classification by interval-valued training data using the triangular kernel ⋮ Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels ⋮ Fuzzy \(K\)-means clustering algorithms for interval-valued data based on adaptive quadratic distances ⋮ Similarity measures for interval-valued fuzzy sets based on average embeddings and its application to hierarchical clustering ⋮ Cluster analysis of census data using the symbolic data approach ⋮ Self-organizing map for symbolic data ⋮ On the construction of an aggregated measure of the development of interval data ⋮ Efficient indexing of interval time sequences ⋮ Kernel-based hard clustering methods in the feature space with automatic variable weighting ⋮ Partitional clustering algorithms for symbolic interval data based on single adaptive distances ⋮ Far beyond the classical data models: symbolic data analysis ⋮ On Central Tendency and Dispersion Measures for Intervals and Hypercubes ⋮ Kernel fuzzy \(c\)-means with automatic variable weighting ⋮ Classification of multivariate objects using interval quantile classes
Cites Work
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- Histograms in symbolic data analysis
- Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
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