A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities

DOI10.1016/j.patcog.2008.08.037zbMath1183.68556OpenAlexW2067021889MaRDI QIDQ1015175

Mohamed A. Ismail, Noha A. Yousri, Mohamed S. Kamel

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.2008.08.037

zbMATH Keywords

clusteringdynamic modelhigh dimensional dataarbitrary density clustersarbitrary shaped clustersdistance-relatedness

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (2)

A novel fast heuristic to handle large-scale shape clustering ⋮ Kernel-based mixture models for classification

Uses Software

• ElemStatLearn
• clusfind
• ROCK

Cites Work

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• Soft clustering of multidimensional data: A semi-fuzzy approach
• Graph theoretical clustering based on limited neighbourhood sets
• Cure: An efficient clustering algorithm for large databases
• Finding Groups in Data
• Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
• The elements of statistical learning. Data mining, inference, and prediction

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