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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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Bayesian K-means Clustering

Catatan metode taksonomi · latent-structure / statistics

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. · URL
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Chapter 9 (Mixture models and EM) and Chapter 10 (Approximate Inference). · ISBN 978-0387310732

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Taxonomic bucketBayesian Cluster Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Hierarchical Clusteringmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Mixture Modelingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketCluster Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketLatent Class Analysismachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMixture Modelingmachine-suggested · Relational suggestion, not evidence.