Publikation: Neighborgram Clustering Interactive Exploration of Cluster Neighborhoods
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2002
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2002 IEEE International Conference on Data Mining, 2002. Proceedings.. IEEE Comput. Soc, 2002, pp. 581-584. ISBN 0-7695-1754-4. Available under: doi: 10.1109/ICDM.2002.1184004
Zusammenfassung
We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize, select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically, allowing for an automatic or semi-automatic clustering process where the user only occasionally interacts with the algorithm. We illustrate the ability to automatically identify and visualize clusters using NCI s AIDS Antiviral Screen data set.
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2002 IEEE International Conference on Data Mining. ICDM 2002, Maebashi City, Japan
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BERTHOLD, Michael R., Bernd WISWEDEL, David E. PATTERSON, 2002. Neighborgram Clustering Interactive Exploration of Cluster Neighborhoods. 2002 IEEE International Conference on Data Mining. ICDM 2002. Maebashi City, Japan. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings.. IEEE Comput. Soc, 2002, pp. 581-584. ISBN 0-7695-1754-4. Available under: doi: 10.1109/ICDM.2002.1184004BibTex
@inproceedings{Berthold2002Neigh-5476,
year={2002},
doi={10.1109/ICDM.2002.1184004},
title={Neighborgram Clustering Interactive Exploration of Cluster Neighborhoods},
isbn={0-7695-1754-4},
publisher={IEEE Comput. Soc},
booktitle={2002 IEEE International Conference on Data Mining, 2002. Proceedings.},
pages={581--584},
author={Berthold, Michael R. and Wiswedel, Bernd and Patterson, David E.}
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<dcterms:abstract xml:lang="eng">We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize, select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically, allowing for an automatic or semi-automatic clustering process where the user only occasionally interacts with the algorithm. We illustrate the ability to automatically identify and visualize clusters using NCI s AIDS Antiviral Screen data set.</dcterms:abstract>
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