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Clustering graphs annotated with feature vectors has recently gained much attention. The goal is to detect groups of vertices that are densely connected in the graph as well as similar with respect to their feature values. While early approaches treated all dimensions of the feature space as equally important, more advanced techniques consider the varying relevance of dimensions for different groups...
Spectral clustering (SC) has become one of the most popular clustering methods. Given an affinity matrix, SC explores its spectral-graph structure to partition data into disjoint meaningful groups. However, in many applications, there are multiple potentially useful features and thereby multiple affinity matrices. For applying spectral clustering to such cases, these affinity matrices must be aggregated...
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