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An Optimum Model for the Retrieval of Missing Values for Data Cleansing using Regression Analysis

by Deepshikha Aggarwal, V. B. Aggarwal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 2
Year of Publication: 2015
Authors: Deepshikha Aggarwal, V. B. Aggarwal
10.5120/20529-2869

Deepshikha Aggarwal, V. B. Aggarwal . An Optimum Model for the Retrieval of Missing Values for Data Cleansing using Regression Analysis. International Journal of Computer Applications. 117, 2 ( May 2015), 35-39. DOI=10.5120/20529-2869

@article{ 10.5120/20529-2869,
author = { Deepshikha Aggarwal, V. B. Aggarwal },
title = { An Optimum Model for the Retrieval of Missing Values for Data Cleansing using Regression Analysis },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 2 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number2/20529-2869/ },
doi = { 10.5120/20529-2869 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:17.564998+05:30
%A Deepshikha Aggarwal
%A V. B. Aggarwal
%T An Optimum Model for the Retrieval of Missing Values for Data Cleansing using Regression Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 2
%P 35-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An important aspect of the data mining is the pre-processing of the data. Pre-processing of the data is important because real world data is susceptible to inconsistencies, noise and missing values. Such a data cannot be used in data mining as that would produce highly inadequate results . There are basically two methods through which we can remove the problem of the missing values the first one is to ignore the data set with the missing value the second one is to predict those values. Prediction can be made based on assuming the continuity of the data or giving them some suitable value based on previous knowledge . In this paper our focus is on providing an adequate method to fill those missing values by predicting a suitable value by comparing and choosing a suitable regression method based on both the statistical and the subjective analysis of the graph from the various known regression method.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Quality Missing Values Data Cleaning Regression Linear Quadratic Exponential Gaussian Prediction RMSE.

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