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Incremental Missing Value Replacement Techniques for Stream Data

by Kinnari Patel, R G Mehta, M M Raghuvanshi, N N Vadnere
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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, N N Vadnere . Incremental Missing Value Replacement Techniques for Stream Data. International Journal of Computer Applications. 122, 17 ( July 2015), 9-13. DOI=10.5120/21791-5129

@article{ 10.5120/21791-5129,
author = { Kinnari Patel, R G Mehta, M M Raghuvanshi, N N Vadnere },
title = { Incremental Missing Value Replacement Techniques for Stream Data },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 17 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number17/21791-5129/ },
doi = { 10.5120/21791-5129 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:48.180181+05:30
%A Kinnari Patel
%A R G Mehta
%A M M Raghuvanshi
%A N N Vadnere
%T Incremental Missing Value Replacement Techniques for Stream Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 17
%P 9-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Skewness Mean Median Standard deviation Discretization.

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