A Monte Carlo evaluation of the moving method, \(k\)-means and two self-organising neural networks

DOI10.1007/BF01237937zbMath0911.68173MaRDI QIDQ1271314

J. A. Long, E. W. Tyree

Publication date: 4 May 1999

Published in: PAA. Pattern Analysis and Applications (Search for Journal in Brave)

zbMATH Keywords

Monte Carlo simulationcluster analysis\(k\)-meansmoving method

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Multidimensional data clustering utilizing hybrid search strategies
• The weighted sum of split and diameter clustering
• Monte Carlo comparisons of selected clustering procedures
• Strong consistency of k-means clustering
• Self-organized formation of topologically correct feature maps
• An algorithm to maximize the agreement between partitions
• Comparative performance of the FSCL neural net and \(K\)-means algorithm for market segmentation
• A comparison of SOM neural network and hierarchical clustering methods
• Heuristic and optimization approaches to extending the Kohonen self organizing algorithm
• Neural networks: A review from a statistical perspective. With comments and a rejoinder by the authors
• A study of the classification capabilities of neural networks using unsupervised learning: A comparison with \(K\)-means clustering
• The Kohonen self-organizing map method: An assessment
• A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm
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