Efficient Visualization of Association Rule Mining Using the Trie of Rules
Year: 2024
Type: conference-paper
Abstract: Association Rule Mining (ARM) is a popular technique in data mining and machine learning for uncovering meaningful relationships within large datasets. However, the extensive number of generated rules presents significant challenges for interpretation and visualization. Effective visualization must not only be clear and informative but also efficient and easy to learn. Existing visualization methods often fall short in these areas. In response, we propose a novel visualization technique called the ”Trie of Rules.” This method adapts the Frequent Pattern Tree (FP-tree) structure to visualize association rules efficiently, capturing extensive information while maintaining clarity. Our approach reveals hidden insights such as clusters and substitute items, and introduces a unique feature for calculating confidence in rules with compound consequents directly from the graph structure. We conducted a comprehensive evaluation using a survey where we measured cognitive load to calculate the efficiency and learnability of our methodology. The results indicate that our method significantly enhances the interpretability and usability of ARM visualizations.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404579386