Improving K-means method via shrinkage estimation and LVQ algorithm

Abstract

Clustering is an important task in statistics and many other scientific fields. In this note, we propose an improved K-means clustering approach called ‘enhanced shrinkage K-means’ based on the James-Stein estimator and learning vector quantization (LVQ) algorithm. The basic idea of this new algorithm is taking into account of the strength of both unsupervised clustering and supervised classification methods, in which we shrink the clustering centers toward the prototype vector via James-Stein estimator. We carry out extensive simulation studies and real data analysis to evaluate the performance of this new approach, and obtain encouraging results.

Keywords:

• Clustering
• K-means
• LVQ
• shrinkage estimator
• Supervised learning
• Unsupervised learning

Acknowledgments

We are very grateful to the editor and the reviewer for helpful comments and suggestions which have improved the presentation of the paper. This work is supported by the National Natural Science Foundation of China (11571154), the Fundamental Research Funds for the Central Universities (lzujbky-2018-110) and the Major Program of the National Social Science Fund of China (17ZDA092).

Notes

1 http://archive.ics.uci.edu/ml

1 http://archive.ics.uci.edu/ml