James-Stein shrinkage to improve k-means cluster analysis

Jinxin Gao and David B. Hitchcock

Computational Statistics & Data Analysis, 2010, vol. 54, issue 9, 2113-2127

Abstract: We study a general algorithm to improve the accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters toward the overall mean of all data using a James-Stein-type adjustment, and then the James-Stein shrinkage estimators act as the new centroids in the next clustering iteration until convergence. We compare the shrinkage results to the traditional k-means method. A Monte Carlo simulation shows that the magnitude of the improvement depends on the within-cluster variance and especially on the effective dimension of the covariance matrix. Using the Rand index, we demonstrate that accuracy increases significantly in simulated data and in a real data example.

Keywords: Centroids; Effective; dimension; k-means; clustering; Stein; estimation (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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