Bayesian K-means Clustering
Bayesian K-means clustering extends the classical K-means algorithm by placing prior distributions over cluster centroids and mixing proportions. This probabilistic framework provides uncertainty estimates for cluster assignments, allows principled model selection for the number of clusters, and regularises centroid estimation — especially valuable when data are scarce or high-dimensional.
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Izvori
- Kulis, B. & Jordan, M. I. (2012). Revisiting k-means: New algorithms via Bayesian nonparametrics. In Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, pp. 513–520. link ↗
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Chapter 9 (Mixture models and EM) and Chapter 10 (Approximate Inference). ISBN: 978-0387310732
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Bayesian K-means Clustering. ScholarGate. https://scholargate.app/sr/statistics/bayesian-k-means-clustering
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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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