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A Computational Study of Replicated Clustering with an Application to Market Segmentation *
Author(s) -
Helsen Kristiaan,
Green Paul E.
Publication year - 1991
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1991.tb01910.x
Subject(s) - cluster analysis , computer science , monte carlo method , set (abstract data type) , hierarchical clustering , market segmentation , data mining , selection (genetic algorithm) , sample (material) , process (computing) , algorithm , machine learning , mathematics , statistics , marketing , chemistry , chromatography , business , programming language , operating system
In most commercial applications of k ‐means clustering, researchers choose one set of k seed points to start the partitioning process; often, the initial set of seeds is chosen randomly. Using Monte Carlo simulation, we show that significant benefits are associated with replicated starting configurations that incorporate seed selection procedures based on a hierarchical clustering of sample points drawn from the original data matrix. A real‐world application of the approach is then presented.