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Probabilistic Hierarchical Clustering
- Algorithmic hierarchical clustering
- Nontrivial to choose a good distance measure
- Hard to handle missing attribute values
- Optimization goal not clear: heuristic, local search
- Probabilistic hierarchical clustering
- Use probabilistic models to measure distances between clusters
- Generative model: Regard the set of data objects to be clustered as a sample of the underlying data generation mechanism to be analyzed
- Easy to understand, same efficiency as algorithmic agglomerative clustering method, can handle partially observed data
- In practice, assume the generative models adopt common distributions functions, e.g., Gaussian distribution or Bernoulli distribution, governed by parameters
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