Comparative performance of the FSCL neural net and the K-means algorithm for market segmentation

Article Abstract:

The Frequency-Sensitive Competitive Learning (FSCL) neural network and the K-means non-hierarchical clustering method were applied to a cluster analysis of coffee brand choice data to evaluate their performances. Specifically, simulated data and actual brand choice probabilities were compared. The results showed that the combination of the FSCL network and K-means clustering produced the best results in terms of face validity, disparity in cluster sizes and ease of interpretation.

Market segmentation, Cluster analysis

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A genetic algorithm for the set covering problem

Article Abstract:

A heuristic for solving non-unicost set covering problems is introduced. The heuristic, which is based on genetic algorithms (GA), derives high-quality solutions to randomly generated problems of different sizes and densities. Instead of standard GA procedures, it employs a new fitness-based crossover operator, a variable mutation rate and a heuristic feasibility operator tailored for the problem at hand.

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An adaptive, multiple restarts neural network algorithm for graph coloring

Article Abstract:

A scalable neural network algorithm for solving graph coloring problems is introduced. The procedure becomes 'faster and leaner' when it is used because it adaptively decreases the network's size every time it restarts to learn better methods of coloring a graph. Its applications to register allocation, Leighton, modified k-partite, school, Latin square, geometric and random graphs are also discussed.

Graphic methods

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