kDMI

kDMI employs two levels of horizontal partitioning (based on a decision tree and k-NN algorithm) of a data set, in order to find the records that are very similar to the one with missing value/s. Additionally, it uses a novel approach to automatically find the value of k for each record.

Reference

Rahman, M. G. and Islam, M. Z. (2013): kDMI: A Novel Method for Missing Values Imputation Using Two Levels Horizontal Partitioning in a Data set, In Proc. of the 9th International Conference on Advanced Data Mining and Applications(ADMA 13), Hangzhou, China, 14-16 December 2013, pp. 250-263.

BibTeX

@inproceedings{rahman2013kdmi,
  title={kDMI: A novel method for missing values imputation using two levels of horizontal partitioning in a data set},
  author={Rahman, Md Geaur and Islam, Md Zahidul},
  booktitle={Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II 9},
  pages={250--263},
  year={2013},
  organization={Springer}
}

@author Gea Rahman https://csusap.csu.edu.au/~grahman/

Two folders:

1. kDMI_project (NetBeans project)
2. SampleData

kDMI is developed based on Java programming language (jdk1.8.0_211) using NetBeans IDE (8.0.2).

How to run:

1. Open project in NetBeans
2. Run the project

Sample input and output:

run: Please enter the name of the file containing the 2 line attribute information.(example: c:\data\attrinfo.txt)

C:\SampleData\attrinfo.txt

Please enter the name of the data file having missing values: (example: c:\data\data.txt)

C:\SampleData\data.txt

Please enter the name of the output file: (example: c:\data\out.txt)

C:\SampleData\output.txt

Imputation by kDMI is done. The completed data set is written to:

C:\SampleData\output.txt