Mining Frequent Itemsets with Fuzzy Taxonomic Structures for Cybercrime Investigations

Abstract

In the realm of cybercrime investigations, identifying patterns and associations among different entities is a crucial step towards understanding and mitigating criminal activities. Traditional approaches to discovering frequent itemsets typically rely on exact matching between items and lack the ability to handle uncertainty and imprecision in the data. To address this challenge, we propose a method for mining frequent itemsets with fuzzy taxonomic structures in cybercrime investigations. Our approach leverages the concept of fuzzy sets and taxonomies to represent the uncertainty and imprecision in the data, respectively. We demonstrate the effectiveness of our method using a real-world dataset of cybercrime incidents, where we show that our approach can reveal valuable insights into the relationships among different entities involved in cybercrime. Our findings highlight the importance of incorporating fuzzy and taxonomic structures in the analysis of cybercrime data, and suggest new avenues for future research in this area.