An expressive dissimilarity measure for relational clustering using neighbourhood trees
This work addresses the challenge of clustering relational data, which is important for domains like social network analysis, but it appears incremental as it builds on standard clustering methods with a new similarity measure.
The paper tackles the problem of clustering relational data by introducing a novel similarity measure that incorporates multiple types of similarity, such as attributes, relational context, and hypergraph proximity. The experiments show that this measure consistently yields good results across diverse datasets and often outperforms existing methods.
Clustering is an underspecified task: there are no universal criteria for what makes a good clustering. This is especially true for relational data, where similarity can be based on the features of individuals, the relationships between them, or a mix of both. Existing methods for relational clustering have strong and often implicit biases in this respect. In this paper, we introduce a novel similarity measure for relational data. It is the first measure to incorporate a wide variety of types of similarity, including similarity of attributes, similarity of relational context, and proximity in a hypergraph. We experimentally evaluate how using this similarity affects the quality of clustering on very different types of datasets. The experiments demonstrate that (a) using this similarity in standard clustering methods consistently gives good results, whereas other measures work well only on datasets that match their bias; and (b) on most datasets, the novel similarity outperforms even the best among the existing ones.