An initialization method for the \(K\)-means algorithm using neighborhood model

DOI10.1016/j.camwa.2009.04.017zbMath1189.68095OpenAlexW2075098580MaRDI QIDQ980040

Guang Jiang, Fuyuan Cao, J. Y. Liang

Publication date: 28 June 2010

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2009.04.017

zbMATH Keywords

initial cluster centers\(K\)-means clustering algorithmcohesion degreecoupling degreeneighborhood model

Mathematics Subject Classification ID

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

Related Items (7)

New heuristic for harmonic means clustering ⋮ A benchmark calculation for the fuzzy \(c\)-means clustering algorithm: initial memberships ⋮ A new distance with derivative information for functional \(k\)-means clustering algorithm ⋮ An Initialization Method Based on Hybrid Distance for k-Means Algorithm ⋮ Weight selection in \(W-k\)-means algorithm with an application in color image segmentation ⋮ Initialization of \(K\)-modes clustering using outlier detection techniques ⋮ Improved roughk-means clustering algorithm based on weighted distance measure with Gaussian function

Uses Software

• UCI-ml
• apcluster
• APCluster
• LOF

Cites Work

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• Clustering by Passing Messages Between Data Points
• A new measure of uncertainty based on knowledge granulation for rough sets
• Relational interpretations of neighborhood operators and rough set approximation operators
• Neighborhood operator systems and approximations
• Rough set-based clustering with refinement using Shannon's entropy theory
• K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
• A rough set approach to outlier detection

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