@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 {338,
	title = {A novel RDB-SW approach for commodities price dynamic trend analysis based on web mining},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Cited By (since 1996):5Export Date: 10 July 2014},
	month = {2012},
	pages = {230 - 235},
	abstract = {In order to improve the accuracy of price forecasting for dynamic trend analysis on imperfect data by web extracting, a novel repair data algorithm based on dichotomy backfilling is proposed in this paper. The price forecasting algorithm based on the Sliding Window (SW) is utilized to verify the validity of Repair Dichotomy Backfilling (RDB) algorithm. Experiments demonstrated that the mean absolute errors can be achieved to 5.31 percent average. Furthermore, the price forecasting algorithm based on original data and that based on RDB data are explored to compare their accuracy. Experiments demonstrated the reduction of mean absolute errors in the price dynamic trend analysis verification model. The average MAE on original data is 2.38 percent when the average MAE on RDB data is 2.31 percent. Experiment results proved that the proposed RDB-SW approach is meaningful and useful to analyze and to research the price market on imperfect data by Web extracting.},
	keywords = {Dichotomy backfilling, Disturbance factor, Imperfect data, Price forecast algorithm, Sliding window},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866463450\&partnerID=40\&md5=5874dc4739d723f46728438c22a52b9a},
	author = {Zhu, Q. and Zhou, P. and Cao, S. and Yan, Y. and Ding, J.}
}
