NDLI: Density Estimation Technique for Data Stream Classification

Content Provider	IEEE Xplore Digital Library
Author	Kerdprasop, N. Kerdprasop, K.
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Eng., Suranaree Univ. of Technol. (Kerdprasop, N.; Kerdprasop, K.)
Abstract	Density estimation is an important pre-processing step in the problem of data stream classification in which the number of data is overwhelming and the exact data distribution is unknown. We simplify the problem by employing a statistical sampling technique to obtain an approximate solution. With the proposed method, an unbounded large data set can be sampled in a number of random configurations, and that data can be used to describe the data set as a whole. The efficiency of the method depends largely on the ability to draw samples effectively which in turn depends on how close we can estimate the target density. We use finite mixture models to represent the probability density functions of the data stream. Then, we apply the EM algorithm twice to learn the model parameters. The efficiency of our estimation technique has been shown in the experimental results
Sponsorship	CPS
Starting Page	662
Ending Page	666
File Size	199343
Page Count	5
File Format	PDF
ISBN	0769526411
ISSN	15294188
DOI	10.1109/DEXA.2006.49
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-09-04
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sampling methods Data mining Data engineering Probability density function Data analysis Algorithm design and analysis Performance analysis Parameter estimation Distributed computing Knowledge engineering
Content Type	Text
Resource Type	Article

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