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Graph Theoretic Approach to Heterogeneous Data Clustering
Inglês
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Data clustering is the process of automatically§grouping data objects into different groups§(clusters). The contribution of this book is§threefold: homogeneous clustering of images, pairwise§heterogeneous data co-clustering, and high-order§star-structured heterogeneous data co-clustering.§First, we propose a semantic-based hierarchical image§clustering framework based on multi-user feedback. By§treating each user as an independent weak classifier,§we show that§combining multi-user feedback is equivalent to the§combinations of weak independent classifiers. Second,§we present a novel graph theoretic approach to§perform pairwise heterogeneous data co-clustering. We§then propose Isoperimetric Co-clustering Algorithm, a§new method for partitioning the bipartite graph.§Lastly, for high-order heterogeneous co-clustering,§we propose the Consistent Isoperimetric High-Order§Co-clustering framework to address star-structured§co-clustering problems in which a central data type§is connected to all the other data types. We model§this kind of data using a k-partite graph and§partition it by considering it as a fusion of§multiple bipartite graphs.
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