Clustering Multi-represented Objects with Noise

Traditional clustering algorithms are based on one representation space, usually a vector space. However, in a variety of modern applications, multiple representations exist for each object. Molecules for example are characterized by an amino acid sequence, a secondary structure and a 3D representation. In this paper, we present an efficient density-based approach to cluster such multi-represented data, taking all available representations into account. We propose two different techniques to combine the information of all available representations dependent on the application. The evaluation part shows that our approach is superior to existing techniques.