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Aditya Jitta

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AAAI Conference 2018 Conference Paper

On Controlling the Size of Clusters in Probabilistic Clustering

Aditya Jitta
Arto Klami

Classical model-based partitional clustering algorithms, such as k-means or mixture of Gaussians, provide only loose and indirect control over the size of the resulting clusters. In this work, we present a family of probabilistic clustering models that can be steered towards clusters of desired size by providing a prior distribution over the possible sizes, allowing the analyst to ﬁne-tune exploratory analysis or to produce clusters of suitable size for future down-stream processing. Our formulation supports arbitrary multimodal prior distributions, generalizing the previous work on clustering algorithms searching for clusters of equal size or algorithms designed for the microclustering task of ﬁnding small clusters. We provide practical methods for solving the problem, using integer programming for making the cluster assignments, and demonstrate that we can also automatically infer the number of clusters.

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UAI Conference 2016 Conference Paper

Probabilistic Size-constrained Microclustering

Arto Klami
Aditya Jitta

Microclustering refers to clustering models that produce small clusters or, equivalently, to models where the size of the clusters grows sublinearly with the number of samples. We formulate probabilistic microclustering models by assigning a prior distribution on the size of the clusters, and in particular consider microclustering models with explicit bounds on the size of the clusters. The combinatorial constraints make full Bayesian inference complicated, but we manage to develop a Gibbs sampling algorithm that can efficiently sample from the joint cluster allocation of all data points. We empirically demonstrate the computational efficiency of the algorithm for problem instances of varying difficulty.

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