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Mixture Models in Clustering Analysis by Yug Sharma is a document available to read on EtoBox.

What is Mixture Models in Clustering Analysis about?

The document discusses mixture models, which are used to partition heterogeneous datasets into clusters. It outlines various objective functions for clustering, including maximizing distances between different clusters and minimizing distances within the same cluster, leading to the k-means clustering algorithm. Additionally, it introduces a probabilistic framework for clustering, utilizing maximum likelihood estimation and Bayesian inference to assign data points to clusters based on their probabilities.

Author

Yug Sharma

Language

EN