A Clustering Method Based on Soft Learning of Model (Prototype) and Dissimilarity Metrics

DOI10.1007/978-3-540-89985-3_5zbMath1183.68460OpenAlexW1558976027MaRDI QIDQ3628411

Babak Nadjar Araabi, Arash Arami

Publication date: 20 May 2009

Published in: Communications in Computer and Information Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-89985-3_5

zbMATH Keywords

clusteringfuzzy membership functioncluster prototypelinear prototypemass prototypeshell prototype

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Cites Work

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• Partitional fuzzy clustering methods based on adaptive quadratic distances
• A survey of fuzzy clustering
• Enhancement of fuzzy clustering by mechanisms of partial supervision
• Fuzzy clustering with squared Minkowski distances
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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