Probabilistic clustering of time-evolving distance data Journal Article
| Authors: | Vogt, J. E.; Kloft, M.; Stark, S.; Raman, S. S.; Prabhakaran, S.; Roth, V.; Rätsch, G. |
| Article Title: | Probabilistic clustering of time-evolving distance data |
| 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. © 2015, The Author(s). |
| Keywords: | cluster analysis; artificial intelligence; software engineering; cluster structure; clustering methods; dirichlet process; dynamic clustering; number of clusters; pairwise distances; probabilistic clustering; state of the art |
| Journal Title: | Machine Learning |
| Volume: | 100 |
| Issue: | 2-3 |
| ISSN: | 0885-6125 |
| Publisher: | Springer |
| Date Published: | 2015-09-01 |
| Start Page: | 635 |
| End Page: | 654 |
| Language: | English |
| DOI: | 10.1007/s10994-015-5516-x |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Export Date: 2 September 2015 -- Source: Scopus |
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