Abstract
Data streams have recently attracted attention for their applicability to numerous domains including credit fraud detection, network intrusion detection, and click streams. Stream clustering is a technique that performs cluster analysis of data streams that is able to monitor the results in real time. A data stream is continuously generated sequences of data for which the characteristics of the data evolve over time. A good stream clustering algorithm should recognize such evolution and yield a cluster model that conforms to the current data. In this paper, we propose a new technique for stream clustering which supports five evolutions that are appearance, disappearance, self-evolution, merge and split.
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Udommanetanakit, K., Rakthanmanon, T., Waiyamai, K. (2007). E-Stream: Evolution-Based Technique for Stream Clustering. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_58
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DOI: https://doi.org/10.1007/978-3-540-73871-8_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73870-1
Online ISBN: 978-3-540-73871-8
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