A constraint-based approach for multispace clustering

In many applications, a set of objects can be represented by different points of view (universes). Beside numeric, ordinal and nominal features, objects may be represented using spatio-temporal information, sequences, and more complex structures (e.g., graphs). Learning from all these different spaces is challenging, since often di erent algorithms and metrics are needed. In the case of data clustering, a partitional, hierarchical or density-based algorithm is often well suited for a speci c type of data, but not for other ones. In this work we present a preliminary study on a framework that tries to link different clustering results by exploiting pairwise similarity constraints. We propose two algorithmic settings, and we present an application to a real-world dataset of trajectories.