Probabilistic clustering of time-evolving distance data

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....

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Bibliographic Details
Published in:Machine learning 2015-09, Vol.100 (2-3), p.635-654
Main Authors: Vogt, Julia E., Kloft, Marius, Stark, Stefan, Raman, Sudhir S., Prabhakaran, Sandhya, Roth, Volker, Rätsch, Gunnar
Format: Article
Language:English
Subjects:
Artificial Intelligence
Brain
Clustering
Clusters
Computer Science
Control
Dirichlet problem
Dynamic tests
Evolution
Mechatronics
Natural Language Processing (NLP)
Probabilistic methods
Probability
Probability theory
Robotics
Simulation and Modeling
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Summary: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.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-015-5516-x