Article ID Journal Published Year Pages File Type
535059 Pattern Recognition Letters 2007 8 Pages PDF
Abstract

We present a genetic algorithm for selecting centers to seed the popular k-means method for clustering. Using a novel crossover operator that exchanges neighboring centers, our GA identifies superior partitions using both benchmark and large simulated data sets.

Keywords
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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A genetic algorithm that exchanges neighboring centers for k-means clustering
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