Abstract—The Association Rules Discovery is a technique widely used for various objectives. One is for Classification Based on Associations (CBA) with Class Association Rules (CARs). The number of rules discovered from data is extremely high with exponential numbers related to item types in the data. Thus, pruning uninteresting rules is a very important task with this technique. In the traditional technique, minimum Support and minimum Confidence are the main interestingness measures defined by the user for pruning tasks. However, some interesting rules have low Support or Confidence and are pruned at the same time as uninteresting rules. This problem usually occurs with an imbalanced Class ratio in a dataset such as the Scientific or Health dataset, positive-Class CARs usually have a much smaller number than negative-Class CARs. Positive-Class CARs are usually found to have low Support or Confidence that need trust in use without uninteresting rules. In this paper, we describe this problem in relation to a breast cancer dataset, and use a pruning task to discover interesting positive CARs even with low Support or Confidence. We propose a new measure called the Profitability-of-Interestingness Measure (PoI) to prune positive-class CARs from the dataset. Performance is measured by accuracy, precision, and recall. The results show that Pruned CARs have similar accuracy to traditional CARs. A comparison of the same rules for CBA Classifiers shows that Pruned CARs offer more precision than traditional CARs. The Pruned CARs set is more concise and easier to understand because of the lower number of confusing rules.
 

Index Terms—association rules pruning, class association rules, interestingness measure, profitability of interestingness

Cite: Peera Liewlom, "Class-Association-Rules Pruning by the Profitability-of-Interestingness Measure: Case Study of an Imbalanced Class Ratio in a Breast Cancer Dataset," Journal of Advances in Information Technology, Vol. 12, No. 3, pp. 246-252, August 2021. doi: 10.12720/jait.12.3.246-252

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