Abstract
The data stream domain has become increasingly important in recent years because of its applicability to a wide variety of applications. Problems such as data mining and privacy preservation which have been studied for traditional data sets cannot be easily solved for the data stream domain. This is because the large volume of data arriving in a stream renders most algorithms to inefficient as most mining and privacy preservation algorithms require multiple scans of data which is unrealistic for stream data. More importantly, the characteristics of the data stream can change over time and the evolving pattern needs to be captured. In this talk, I’ll discuss the issued and focus on how to mine evolving data streams and preserve privacy.
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© 2004 Springer-Verlag Berlin Heidelberg
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Yu, P.S. (2004). Mining of Evolving Data Streams with Privacy Preservation. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_1
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DOI: https://doi.org/10.1007/978-3-540-24775-3_1
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