Abstract
The three main requirements for clustering data streams on-line are one pass over the data, high processing speed, and consuming a small amount of memory. We propose an algorithm that can fulfill these requirements by introducing an incremental grid data structure to summarize the data streams on-line. In order to deal with high-dimensional problems, the algorithm adopts a simple heuristic method to select a subset of dimensions on which all the operations for clustering are performed. Our performance study with a real network intrusion detection stream data set demonstrates the efficiency and effectiveness of our proposed algorithm.
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Lu, Y., Sun, Y., Xu, G., Liu, G. (2005). A Grid-Based Clustering Algorithm for High-Dimensional Data Streams. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_97
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DOI: https://doi.org/10.1007/11527503_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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