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- W2100094417 abstract "In recent years, the complexity of data objects in data mining applications has increased as well as their plain numbers. As a result, there exist various feature transformations and thus multiple object representations. For example, an image can be described by a text annotation, a color histogram and some texture features. To cluster thesemulti-representedobjects,dedicateddataminingalgorithms have been shown to achieve improved results. In this paper, we will therefore introduce a method for hierarchical density-based clustering of multi-represented objects which is insensitive w.r.t. the choice of parameters. Furthermore, we will introduce a theoretical model that allows us to draw conclusions about the interaction of representations. Additionally, we will show how these conclusions can be used for defining a suitable combination method for multiple representations. To back up the usability of our proposed method, we present encouraging results for clustering a real world image data set that is described by 4 dierent representations." @default.
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- W2100094417 date "2005-01-01" @default.
- W2100094417 modified "2023-09-26" @default.
- W2100094417 title "Hierarchical Density-Based Clustering for Multi-Represented Objects" @default.
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