Publikation: Interactive Exploration of Fuzzy Clusters Using Neighborgrams
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We describe an interactive method to generate a set of fuzzy clusters for classes of interest of a given, labeled data set. The presented method is therefore best suited for applications where the focus of analysis lies on a model for the minority class or for small- to medium-size data sets. The clustering algorithm creates one-dimensional models of the neighborhood for a set of patterns by constructing cluster candidates for each pattern of interest and then chooses the best subset of clusters that form a global model of the data. The accompanying visualization of these neighborhoods allows the user to interact with the clustering process by selecting, discarding, or fine-tuning potential cluster candidates. Clusters can be crisp or fuzzy and the latter leads to a substantial improvement of the classification accuracy. We demonstrate the performance of the underlying algorithm on several data sets from the StatLog project.
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WISWEDEL, Bernd, David E. PATTERSON, Michael R. BERTHOLD, 2003. Interactive Exploration of Fuzzy Clusters Using Neighborgrams. 12th IEEE International Conference on Fuzzy Systems. St. Louis, Missouri, USA, 25. Mai 2003 - 28. Mai 2003. In: NASRAOUI, Olfa, ed. and others. Proceedings of the 12th IEEE International Conference on Fuzzy Systems. Piscataway, NJ: IEEE Service Center, 2003, pp. 660-665. ISBN 0-7803-7810-5. Available under: doi: 10.1109/FUZZ.2003.1209442BibTex
@inproceedings{Wiswedel2003Inter-24374, year={2003}, doi={10.1109/FUZZ.2003.1209442}, title={Interactive Exploration of Fuzzy Clusters Using Neighborgrams}, isbn={0-7803-7810-5}, publisher={IEEE Service Center}, address={Piscataway, NJ}, booktitle={Proceedings of the 12th IEEE International Conference on Fuzzy Systems}, pages={660--665}, editor={Nasraoui, Olfa}, author={Wiswedel, Bernd and Patterson, David E. and Berthold, Michael R.} }
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