Abstract
Data warehouses are a challenging field of application for data mining tasks such as clustering. Usually, updates are collected and applied to the data warehouse periodically in a batch mode. As a consequence, all mined patterns discovered in the data warehouse (e.g. clustering structures) have to be updated as well. In this paper, we present a method for incrementally updating the clustering structure computed by the hierarchical clustering algorithm OPTICS. We determine the parts of the cluster ordering that are affected by update operations and develop efficient algorithms that incrementally update an existing cluster ordering. A performance evaluation of incremental OPTICS based on synthetic datasets as well as on a real-world dataset demonstrates that incremental OPTICS gains significant speed-up factors over OPTICS for update operations.
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Kriegel, HP., Kröoger, P., Gotlibovich, I. (2003). Incremental OPTICS: Efficient Computation of Updates in a Hierarchical Cluster Ordering. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_23
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DOI: https://doi.org/10.1007/978-3-540-45228-7_23
Publisher Name: Springer, Berlin, Heidelberg
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