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Incremental Missing Value Replacement Techniques for Stream Data

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 17
Year of Publication: 2015
Authors:
Kinnari Patel
R G Mehta
M M Raghuvanshi
N N Vadnere
10.5120/21791-5129

Kinnari Patel, R G Mehta, M M Raghuvanshi and N N Vadnere. Article: Incremental Missing Value Replacement Techniques for Stream Data. International Journal of Computer Applications 122(17):9-13, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Kinnari Patel and R G Mehta and M M Raghuvanshi and N N Vadnere},
	title = {Article: Incremental Missing Value Replacement Techniques for Stream Data},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {17},
	pages = {9-13},
	month = {July},
	note = {Full text available}
}

Abstract

Stream data mining is the process of excerpting knowledge structure from large, continuous data. For stream data, various techniques are proposed for preparing the data for data mining task. In recent years stream data have become a growing area for the researcher, but there are many issues occurring in classifying these data due to erroneous and noisy data. Change of trend in the data periodically produces major challenge for data miners. This research concentrates on incremental missing value replacement for stream data. The proposed method generates the value for the missing data considering the data type and data distribution. It also considers the concept drift in the data stream. The method is applied to different datasets and promising results derived.

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