Data reduction technique in fuzzy association rule mining

This submission contains a technique to decrease the processed-data size in fuzzy association rule mining (ARM).

This submission is to support the submission of the corresponding paper. The paper analyzes the data reduction technique proposed in https://doi.org/10.1016/j.eswa.2020.113781.

The submission has 7 main scripts to be launched in the following order:

1) Initial_dataset_processing % the script performs clustering by fuzzy C-means to obtain a reduced-size "Data_KM_Final_Set.txt". If no mindist is required, use mindist=999

or

Initial_dataset_processing_KM % the script performs clustering by K-means to obtain a reduced-size "Data_KM_Final_Set.txt". If no mindist is required, use mindist=999

2) Initial_dataset_formalization % the script prepares a dataset for further ARM.

3) MF_Show % the script plots the results of partitioning.

4) Critical_C=???? % the operation defines minsupp in ARM.

5) Entire_Ruleset_Design % the script performs ARM.

6) FIS_Design % the script creates a Mamdani-Type FIS from the ARM results.

7) FIS_Running % the created FIS is tested on selected data.

Note: this submission is a variation of https://www.mathworks.com/matlabcentral/fileexchange/73104 and, possibly, will be merged with it in the future.