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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Deepali A. Patil
Thadomal Shahani Engineering College
Computer Science & IT Electrical & Electronics Engineering

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KNN and ARL Based Imputation to Estimate Missing Values Thirumahal R; Deepali A. Patil
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 2, No 3: September 2014
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (233.74 KB) | DOI: 10.52549/ijeei.v2i3.117

Abstract

Missing data are the absence of data items for a subject; they hide some information that may be important. In practice, missing data have been one major factor affecting data quality. Thus, Missing value imputation is needed. Methods such as hierarchical clustering and K-means clustering are not robust to missing data and may lose effectiveness even with a few missing values. Therefore, to improve the quality of data method for missing value imputation is needed. In this paper KNN and ARL based Imputation are introduced to impute missing values and accuracy of both the algorithms are measured by using normalized root mean sqare error. The result shows that ARL is more accurate and robust method for missing value estimation.
Co-Authors Thirumahal R