A Bayesian Method for Joint Clustering of Vectorial Data and Network Data

TL;DR

This paper introduces a Bayesian probabilistic model that simultaneously clusters objects based on both their feature vectors and network connections, outperforming traditional methods that handle these data types separately.

Contribution

The paper presents a novel integrative Bayesian clustering model combining Gaussian mixture and stochastic block models for joint analysis of vector and network data.

Findings

The method outperforms existing approaches on synthetic data.

It effectively integrates feature and network information for better clustering.

Results demonstrate significant improvement in clustering accuracy.

Abstract

We present a new model-based integrative method for clustering objects given both vectorial data, which describes the feature of each object, and network data, which indicates the similarity of connected objects. The proposed general model is able to cluster the two types of data simultaneously within one integrative probabilistic model, while traditional methods can only handle one data type or depend on transforming one data type to another. Bayesian inference of the clustering is conducted based on a Markov chain Monte Carlo algorithm. A special case of the general model combining the Gaussian mixture model and the stochastic block model is extensively studied. We used both synthetic data and real data to evaluate this new method and compare it with alternative methods. The results show that our simultaneous clustering method performs much better. This improvement is due to the power of the model-based probabilistic approach for efficiently integrating information.