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Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain

Abstract : Data Clustering has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on partitioning one modality or representation of the data. In this study, we delineate and demonstrate a new, enhanced data clustering approach whose innovation is its exploitation of multiple data modalities. We propose BI-NMF, a bi-modal clustering approach based on Non Negative Matrix Factorization (NMF) that clusters two differing data modalities simultaneously. The strength of our approach is its combining of multiple aspects of the data when forming the final clusters. To assess the utility of our approach, we performed several experiments on two distinct biomedical datasets with two modalities each. Comparing the clusters of BI-NMF with NMF clusters of single data modality, we observed consistent performance enhancement across both datasets. Our experimental results suggest that BI-NMF is advantageous for boosting data clustering.
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Martin Schultz, Michael Krauthammer, Samah Fodeh, Ali Haddad, Cynthia Brandt. Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.357-367, ⟨10.1007/978-3-642-33409-2_37⟩. ⟨hal-01521399⟩



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