A Random Finite Set Model for Data Clustering
It addresses clustering for applications where data are sets rather than vectors, with incremental improvements in handling unknown cluster counts.
The paper tackles clustering of set-valued data with an unknown number of clusters by proposing a Dirichlet Process mixture of Poisson random finite sets model, which automatically learns clusters and parameters, demonstrating effectiveness in discovering unbalanced clusters.
The goal of data clustering is to partition data points into groups to minimize a given objective function. While most existing clustering algorithms treat each data point as vector, in many applications each datum is not a vector but a point pattern or a set of points. Moreover, many existing clustering methods require the user to specify the number of clusters, which is not available in advance. This paper proposes a new class of models for data clustering that addresses set-valued data as well as unknown number of clusters, using a Dirichlet Process mixture of Poisson random finite sets. We also develop an efficient Markov Chain Monte Carlo posterior inference technique that can learn the number of clusters and mixture parameters automatically from the data. Numerical studies are presented to demonstrate the salient features of this new model, in particular its capacity to discover extremely unbalanced clusters in data.