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arXiv · 1710.02101

Reliable Clustering of Bernoulli Mixture Models

Amir Najafi·Abolfazl Motahari·Hamid R. Rabiee
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Abstract

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. To the best of our knowledge, these findings are the first non-asymptotic bounds on the sample complexity of learning or clustering BMMs.

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Amir Najafi, Abolfazl Motahari, Hamid R. Rabiee. 2019-06-16. Reliable Clustering of Bernoulli Mixture Models. https://arxiv.org/abs/1710.02101

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