Abstract
When clustering complex objects, there often exist various feature transformations and thus multiple object representations. To cluster multi-represented objects, dedicated data mining algorithms have been shown to achieve improved results. In this paper, we will introduce combination trees for describing arbitrary semantic relationships which can be used to extend the hierarchical clustering algorithm OPTICS to handle multi-represented data objects. To back up the usability of our proposed method, we present encouraging results on real world data sets.
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Achtert, E., Kriegel, HP., Pryakhin, A., Schubert, M. (2006). Clustering Multi-represented Objects Using Combination Trees. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_21
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DOI: https://doi.org/10.1007/11731139_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
Online ISBN: 978-3-540-33207-7
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