Comparative Study of Three Imputation Methods to Treat Missing Values

Authors

  • Rahul Singhai IIPS, Devi Ahilya Vishwavidyalaya, Indore

DOI:

https://doi.org/10.24297/ijct.v11i7.3472

Keywords:

Knowledge Discovery In database, Data mining, Imputation methods, Sampling. Attribute missing values, Data preprocessing.

Abstract

One relevant problem in data preprocessing is the presence of missing data that leads the poor quality of patterns, extracted after mining. Imputation is one of the widely used procedures that replace the missing values in a data set by some probable values. The advantage of this approach is that the missing data treatment is independent of the learning algorithm used. This allows the user to select the most suitable imputation method for each situation. This paper analyzes the various imputation methods proposed in the field of statistics with respect to data mining. A comparative analysis of three different imputation approaches which can be used to impute missing attribute values in data mining are given that shows the most promising method. An artificial input data (of numeric type) file of 1000 records is used to investigate the performance of these methods. For testing the significance of these methods Z-test approach were used.

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Author Biography

Rahul Singhai, IIPS, Devi Ahilya Vishwavidyalaya, Indore

Designation: Assistant Professor (Senior Scale)INTERNATIONAL INSTITUTE OF PROFESSIONAL STUDIES (IIPS)DEVI AHILYA UNIVERSITYKHANDWA ROAD, INDORE(M.P)INDIA

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Published

2013-11-17

How to Cite

Singhai, R. (2013). Comparative Study of Three Imputation Methods to Treat Missing Values. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 11(7), 2779–2786. https://doi.org/10.24297/ijct.v11i7.3472

Issue

Section

Research Articles