01531nas a2200193   4500008004100000245008300041210006900124260000900193300001400202490000700216520086100223653002801084653001601112653002901128653003301157653001901190100001101209856011701220       2012                        eng d00aA novel method of data stream classification based on incremental storage tree0 anovel method of data stream classification based on incremental   c2012  a301 - 3070 v103 aFor 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.10aBayesian classification10aData stream10aIncremental storage tree10aPrincipal component analysis10aSliding window1 aXu, C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870803724&partnerID=40&md5=ee151686cdc13a9a1a785eb65f87d46701671nas a2200229   4500008004100000245009000041210006900131260000900200300001200209490000600221520094500227653001601172653002701188653002101215653002201236653001901258100001301277700001101290700001101301700001201312856011701324       2006                        eng d00aContinuously evaluating approximate similarity search on high dimensional data stream0 aContinuously evaluating approximate similarity search on high di  c2006  a82 - 860 v43 aIn 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.10aData stream10aHigh dimensional space10aQuery processing10aSimilarity Search10aVideo indexing1 aWang, W.1 aLi, J.1 aAi, C.1 aShi, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33748267270&partnerID=40&md5=da6323dddf161caf008918184cbec115