Reliable Clustering of Bernoulli Mixture Models

Understand

A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions.

• The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks.
• In this paper, we analyze the clusterability of BMMs from a theoretical perspective, when the number of clusters is unknown.
• In particular, we stipulate a set of conditions on the sample complexity and dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a dataset.