Association rules are useful to discover relationships, which are mostly hidden, between the different items in large datasets. Symbolic models are the principal tools to extract association rules. This basic technique is time-consuming, and it generates a big number of associated rules. To overcome this drawback, we suggest a new method, called MARC, to extract the more important association rules of two important levels: Type I, and Type II. This approach relies on multi topographic unsupervised neural network model as well as clustering quality measures that evaluate the success of a given numerical classification model to behave as a natural symbolic model.
Association Rules Unsupervised Learning Multi-SOM Model Symbolic Model Clustering Model Numerical Dataset
Primary Language | English |
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Subjects | Engineering |
Journal Section | Articles |
Authors | |
Publication Date | July 31, 2021 |
Submission Date | April 27, 2021 |
Published in Issue | Year 2021 Volume: 5 Issue: 1 |