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Abstract: Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large... (Update)
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BibTeX entry: (Update)
Hulten, G., Spencer, L., and Domingos, P. Mining time-changing data streams. KDD-01, San Francisco, CA, 2001. http://citeseer.ist.psu.edu/hulten01mining.html More
@inproceedings{ hulten-mining,
author = "G. Hulten and L. Spencer and P. Domingos",
title = "Mining Time-Changing Data Streams"
booktitle="Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
year="2001",
pages="97-106",
address="San Francisco, CA",
publisher="ACM Press",
url = "citeseer.ist.psu.edu/hulten01mining.html" }
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