Shared farthest neighbor approach to clustering of high dimensionality, low cardinality data

DOI10.1016/j.patcog.2006.06.021zbMath1103.68782OpenAlexW2167118705WikidataQ59538698 ScholiaQ59538698MaRDI QIDQ850147

Stefano Rovetta, Francesco Masulli

Publication date: 15 November 2006

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.06.021

zbMATH Keywords

data clusteringhigh-dimensional data analysisDNA microarray data analysissimilarity-based clustering

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (4)

A portmanteau local feature discrimination approach to the classification with high-dimensional matrix-variate data ⋮ A hierarchical hesitant fuzzy K-means clustering algorithm ⋮ A new clustering algorithm for coordinate-free data ⋮ A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

Uses Software

• UCI-ml

Cites Work

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• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Constructing hierarchical set systems
• Hierarchical clustering schemes
• 10.1162/15324430260185592
• The Dissimilarity Representation for Pattern Recognition
• On clusterings
• A theory of the learnable
• Detection and Characterization of Cluster Substructure II. Fuzzy c-Varieties and Convex Combinations Thereof
• Finding Groups in Data
• QUADRATIC ASSIGNMENT AS A GENERAL DATA ANALYSIS STRATEGY
• Model-Based Gaussian and Non-Gaussian Clustering
• A deterministic annealing approach to clustering
• Enumeration of Seven-Argument Threshold Functions
• Measures of Association for Cross Classifications
• Self-organizing maps.

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