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PAKDD
2004
ACM

Clustering Multi-represented Objects with Noise

14 years 4 months ago
Clustering Multi-represented Objects with Noise
Abstract. 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.
Karin Kailing, Hans-Peter Kriegel, Alexey Pryakhin
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where PAKDD
Authors Karin Kailing, Hans-Peter Kriegel, Alexey Pryakhin, Matthias Schubert
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