Probability Density Estimation over evolving data streams using tilted parzen window

Shen Hong, Yan Xiao-Long

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

Probability Density Estimation is a very important technology which has been widely used in data mining and data analysis. In this paper, we generalize the traditional Parzen Window method to data streams and propose a new method of Tilted Parzen Window (TPW) for Probability Density Estimation. To adapt to the evolvement of the data streams, we use the tilted window size that is proportional to data's arrival time. instead of the fixed window size. Theoretical analysis shows that the Tilted Parzen Window method is a valid method for estimating the probability density function (pdf) for data streams. We also propose a new strategy for discarding the historical data in data streams. We prove that this strategy can describe the probability density changes more accurately than the conventional discarding strategy. Empirical results on synthetic data set demonstrate the effectiveness and efficiency of this method.

原文English
主出版物標題IEEE Symposium on Computers and Communications 2008, ISCC 2008
頁面585-589
頁數5
DOIs
出版狀態Published - 2008
對外發佈
事件13th IEEE Symposium on Computers and Communications, ISCC 2008 - Marrakech, Morocco
持續時間: 6 7月 20089 7月 2008

出版系列

名字Proceedings - IEEE Symposium on Computers and Communications
ISSN(列印)1530-1346

Conference

Conference13th IEEE Symposium on Computers and Communications, ISCC 2008
國家/地區Morocco
城市Marrakech
期間6/07/089/07/08

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