Reduction of dynamic data in fuzzy association rule mining

版本 1.1.0 (12.3 KB) 作者: Vugar
The toolbox is capable of performing incremental data reduction in fuzzy association rule mining.
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更新时间 2022/12/2

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This submission is to support the submission of the corresponding manuscript and can be briefly described as follows: incrementality is a property of a Data Mining technique/approach to tune its results to some novel data records without having to reprocess the whole dataset again, and this submission provides an incremental version of the data reduction technique proposed in https://doi.org/10.1016/j.eswa.2020.113781.
In general, scripts should be launched in the following order:
1. Subset_Design.m
INFO: A dataset can be divided into several subsets.
2. NonIncremental_Data_Reduction.m or Incremental_Data_Reduction.m
INFO: (a) This step is optional, since the following steps can also be perfomed on not reduced data. (b) In order to perform incremental reduction, at least one non-incremental reduction process has to be performed on the data before getting additional incrementally-processed records. (c) Results of data reduction get saved into “Data_KM_Final_Set.txt”.
3. Data_Preparation_and_Partitioning.m
INFO: One selects a dataset to be put through ARM, defined partitioning and explicitly defines antecedent and consequent attributes.
4. Show_Partitioning.m
INFO: The defined partitioning is shown in a graphical form.
5. ARM.m
INFO: This script performs association rule mining (ARM).
6. Classifier_Design.m
INFO: This script creates an associative classifier.
7. Classifier_Running.m
INFO: This script runs the classifier on some testing data.

引用格式

Vugar (2024). Reduction of dynamic data in fuzzy association rule mining (https://www.mathworks.com/matlabcentral/fileexchange/121283-reduction-of-dynamic-data-in-fuzzy-association-rule-mining), MATLAB Central File Exchange. 检索时间: .

MATLAB 版本兼容性
创建方式 R2017b
兼容 R2017b
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Scripts

版本 已发布 发行说明
1.1.0

The toolbox itself has been submitted after the submission of the corresponding manuscript.

1.0.0