Probabilistic Clustering of Time-Evolving Distance Data

TL;DR

This paper introduces a probabilistic clustering model for time-evolving distance data that automatically determines the number of clusters and tracks their evolution over time, validated on synthetic and real brain cancer data.

Contribution

It presents a novel dynamic clustering approach that handles varying object and cluster counts without prior specification, using a Dirichlet process prior.

Findings

More accurate than existing methods on synthetic data

Effectively captures cluster evolution over time

Automatically determines the number of clusters

Abstract

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time.