Abstract
In this paper, we present a framework for event-driven Clustering Over Multiple Evolving sTreams, which, abbreviated as COMET, monitors the distribution of clusters on multiple data streams and online reports the results. This information is valuable to support corresponding online decisions. Note that as time advances, the data streams are evolving and the clusters they belong to will change. Instead of directly clustering the multiple data streams periodically, COMET applies an efficient cluster adjustment procedure only when it is required. The signal of requiring to do cluster adjustments is defined as an ”event.” We design a mechanism of event detection which employs piecewise linear approximation as the key technique. The piecewise linear approximation is advantageous in that it can not only be performed in real time as the data comes in, but also be able to capture the trend of data. When an event occurs, through split and merge operations we can report the latest clustering results effectively with high clustering quality.
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Yeh, MY., Dai, BR., Chen, MS. (2006). COMET: Event-Driven Clustering over Multiple Evolving Streams. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_83
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DOI: https://doi.org/10.1007/11731139_83
Publisher Name: Springer, Berlin, Heidelberg
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