FEMI

FEMI (Fuzzy Clustering-based Missing Value Imputation Framework) for data preprocessing. FEMI imputes numerical and categorical missing values by making an educated guess based on records that are similar to the record having a missing value. While identifying a group of similar records and making a guess based on the group, it applies a fuzzy clustering approach and our novel fuzzy expectation maximization algorithm.

Reference

Rahman M. G. and Islam M. Z. (2016): Missing Value Imputation using a Fuzzy Clustering based EM Approach, Knowledge and Information Systems, Vol. 46 (2), pp. 389 – 422. DOI: 10.1007/s10115-015-0822-y.

BibTeX

@article{rahman2016missing,
  title={Missing value imputation using a fuzzy clustering-based EM approach},
  author={Rahman, Md Geaur and Islam, Md Zahidul},
  journal={Knowledge and Information Systems},
  volume={46},
  number={2},
  pages={389--422},
  year={2016},
  publisher={Springer}
}

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

Two folders:

1. FEMI_Master (NetBeans project)
2. SampleData

FEMI 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 FEMI is done. The completed data set is written to:

C:\Gea\Research\FEMI\SampleData\output.txt