Analysis of Various Techniques to Handling Missing Value in Dataset

Paper Topic :

Data Mining

Author Name :

Rajnik Vaishnav

Abstract :

Data mining has made a great progress in recent year but the problem of missing data or value has remained great challenge for data mining. data mining is the field of studying experimental data sets for the discovery of interesting and potentially useful relationships. Missing data or value in a datasets can affect the performance of classifier which leads to difficulty of extracting useful information from datasets. There are a number of alternative ways of dealing with missing data. Several methods like Listwise Deletion, Pairwise Deletion, Mean Imputation, Regression Imputation, K-Means Imputation(KMI), Fuzzy K-means clustering Imputation (FKMI), Support Vector Machine Imputation (SVMI) for imputation of missing values using available values in the data set. In this study, different methods are reviewed and compared with their advantages and disadvantages.

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