Probabilistic Clustering of Time-Evolving Distance Data
This addresses the challenge of dynamic clustering in fields like medical analysis, where data changes over time, though it appears incremental as it builds on existing probabilistic clustering frameworks.
The authors tackled the problem of clustering objects represented by pairwise distances that evolve over time, developing a probabilistic model that automatically determines cluster numbers and captures smooth cluster evolution without requiring object identity matching across time points. They demonstrated improved accuracy over state-of-the-art methods on synthetic data and applied it to analyze brain cancer patient evolution.
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.