Abstract
Information visualization is gaining importance in data mining and transactional data has long been an important target for data miners. We propose a novel approach for visualizing transactional data using multiple clustering results for knowledge discovery. This scheme necessitates us to relate different clustering results in a comprehensive manner. Thus we have invented a method for attributing colors to clusters of different clustering results based on minimal transversals. The effectiveness of our method VisuMClust has been confirmed with experiments using artificial and real-world data sets.
Keywords
- Association Rule
- Knowledge Discovery
- Cluster Result
- Baseline Method
- Candidate Group
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Durand, N., Crémilleux, B., Suzuki, E. (2006). Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_7
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DOI: https://doi.org/10.1007/11875604_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
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