Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain.
Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain.
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DOI：
10.1007/978-3-642-33409-2_37
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
Krauthammer M
中科院分区：
文献类型：
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作者：
Fodeh SJ;Haddad A;Brandt C;Schultz M;Krauthammer M
数据聚类在许多不同的应用领域都是一个活跃的研究领域，现有的聚类算法大多集中在划分一个模态或表示的数据。在这项研究中，我们描绘和展示了一个新的，增强的数据聚类方法，其创新之处在于它的开发多种数据模式。我们提出了BI-NMF，一种基于非负矩阵分解（NMF）的双模态聚类方法，可以同时聚类两种不同的数据模态。我们方法的优势在于在形成最终聚类时结合了数据的多个方面。为了评估我们的方法的实用性，我们在两个不同的生物医学数据集上进行了几个实验，每个数据集都有两种模式。将BI-NMF聚类与单一数据模态的NMF聚类进行比较，我们观察到两个数据集的一致性能增强。我们的实验结果表明，BI-NMF是有利于提高数据聚类。
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.