Comparative performance of the FSCL neural net and \(K\)-means algorithm for market segmentation
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Publication:1268170
DOI10.1016/0377-2217(96)00046-XzbMath0912.90190OpenAlexW2003634948MaRDI QIDQ1268170
P. V. Sundar Balakrishnan, Philip A. Lewis, Varghese S. Jacob, Martha C. Cooper
Publication date: 25 May 1999
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0377-2217(96)00046-x
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Marketing, advertising (90B60)
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Fuzzy \(c\)-means clustering and internet portals: a case study ⋮ Comparing SOM neural network with fuzzy \(c\)-means, \(K\)-means and traditional hierarchical clustering algorithms ⋮ A case study of using the generalized K-harmonic means method in decision-making processes ⋮ A Monte Carlo evaluation of the moving method, \(k\)-means and two self-organising neural networks ⋮ A bibliography of neural network business applications research: 1994--1998 ⋮ Comparing performance of feedforward neural nets and \(K\)-means for cluster-based market segmentation ⋮ Integration of self-organizing feature map and K-means algorithm for market segmentation
Uses Software
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- A study of the classification capabilities of neural networks using unsupervised learning: A comparison with \(K\)-means clustering
- Clustering with relational c-means partitions from pairwise distance data
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