Data reduction technique in fuzzy association rule mining

版本 1.1.1 (11.4 KB) 作者: Vugar
This submission contains a technique to decrease the processed-data size in fuzzy association rule mining (ARM).
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更新时间 2022/10/25

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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.

引用格式

Vugar (2024). Data reduction technique in fuzzy association rule mining (https://www.mathworks.com/matlabcentral/fileexchange/119303-data-reduction-technique-in-fuzzy-association-rule-mining), MATLAB Central File Exchange. 检索时间: .

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