A notion of stability for k-means clustering
Copyright policy )A notion of stability for k-means clustering
In this paper, we define and study a new notion of stability for the $k$-means clustering scheme building upon the notion of quantization of a probability measure. We connect this notion of stability to a geometric feature of the underlying distribution of the data, named absolute margin condition, inspired by recent works on the subject.
- National Research University Higher School of Economics Russian Federation
- Aix-Marseille University France
- Aix Marseille Université France
- French National Centre for Scientific Research France
- Centrale Marseille France
FOS: Computer and information sciences, Computer Science - Machine Learning, Classification and discrimination; cluster analysis (statistical aspects), k-means, Mathematics - Statistics Theory, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Statistics Theory (math.ST), stability, Clustering, Machine Learning (cs.LG), FOS: Mathematics, \(k\)-means, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], clustering
FOS: Computer and information sciences, Computer Science - Machine Learning, Classification and discrimination; cluster analysis (statistical aspects), k-means, Mathematics - Statistics Theory, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Statistics Theory (math.ST), stability, Clustering, Machine Learning (cs.LG), FOS: Mathematics, \(k\)-means, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], clustering
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