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A Comparison of Statistical Packages in R Tool to Impute Missing Values
| Content Provider | Semantic Scholar |
|---|---|
| Author | Suganthi, Mrs. D. Dheenathayalan, K. |
| Copyright Year | 2018 |
| Abstract | Data mining has pushed the realm of information technology beyond predictable limits. Missing value is one of the major factor, which can render the obtain result beyond use attained from specific data set by applying data mining technique. There could be numerous reasons for missing values in a data set such as human error, hardware malfunction etc. It is imperative to tackle the labyrinth of missing values before applying any technique of data mining; otherwise, the information extracted from data set containing missing values will lead to the path of wrong decision making. Due to improper handling, the result obtained by the researcher will differ from ones where the missing values are present. Several methods have been, and continue to be, developed to draw inferences from data sets with missing values. In this work, we experimented and results are compared for three methods of imputation of missing values in numerical dataset. We compare MICE (PMM, CART and SAMPLE), HMISC, HOT.DECK, AMELIA, kNN and MISSFOREST, which are likely the most sophisticated imputation methods currently employed for imputing missing values. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iosrjournals.org/iosr-jce/papers/Conf.17031-2017/Volume-5/1.%2001-08.pdf?id=7556 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
