@article {345,
	title = {A novel method of data stream classification based on incremental storage tree},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {301 - 307},
	abstract = {For the characteristics of large number, fast change, high cost of random access of data stream, this paper proposes a Bayesian classification data mining algorithm based on incremental storage tree to handle the problems. Use sliding window to process data stream and divide it into several basic units, apply Principal component analysis (PCA) to compress the data from window and produce dynamic incremental storage tree, use the second power strategy to continuously update several incremental storage tree. Then use multi-classifier integration technology combines with Bayesian classification to produce Bayesian classifier. At last, adjust the weight of classifier by cyclic test, and get high classification accuracy. Detailed simulation analysis demonstrates that the presented algorithm is of high efficiency of space and time and is more stable.},
	keywords = {Bayesian classification, Data stream, Incremental storage tree, Principal component analysis, Sliding window},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870803724\&partnerID=40\&md5=ee151686cdc13a9a1a785eb65f87d467},
	author = {Xu, C.}
}
@article {27,
	title = {Continuously evaluating approximate similarity search on high dimensional data stream},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Export Date: 10 July 2014},
	month = {2006},
	pages = {82 - 86},
	abstract = {In many applications, including online video monitoring system, it is often necessary to find out the most similar sequence, from a sequence set in database, for the high dimensional data stream. Due to its high computation complexity and the restrictions of query processing on data stream, continuously retrieving the similar content on high dimensional data stream is very challenging. The evaluating algorithm is expected to be very fast to match data stream arrival rate. To address this problem, we propose a method called CVNN in this paper. In our method, the sequences in database are represented into the compact summaries, namely CVs, which can be stored in memory. An online algorithm is conducted to transform data stream into a small number of CVs continuously, at the same time the nearest neighbor query is processed periodically based on the similarity of CVs. Experimental results show that our method is fairly effective.},
	keywords = {Data stream, High dimensional space, Query processing, Similarity Search, Video indexing},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33748267270\&partnerID=40\&md5=da6323dddf161caf008918184cbec115},
	author = {Wang, W. and Li, J. and Ai, C. and Shi, S.}
}
