In the sphere of cybercrime investigations, recognizing patterns and associations among various entities is a critical step towards understanding and mitigating criminal activities. Traditional approaches to finding frequent itemsets typically depend exact matching between articles and lack the ability to handle doubt and imprecision in the dossier. To address this challenge, we propose a method for excavating frequent itemsets with fluffy taxonomic structures in cybercrime investigations. Our approach influences the concept of fluffy sets and taxonomies to represent the changeableness and imprecision in the data, individually. We demonstrate the influence of our method using a honest-world dataset of cybercrime occurrence, where we show that our approach can reveal valuable intuitions into the relationships between different bodies involved in cybercrime. Our findings focal point the importance of combining fuzzy and taxonomic structures in the reasoning of cybercrime data, and plan new avenues for future research in this area.
Author(s) Details:
Pratham Batra,
Maharaja
Surajmal Institute of Technology, New Delhi, India.
Praveen
Arora,
Jagan
Institute of Management Studies, New Delhi, India.
Please see the link here: https://stm.bookpi.org/RATMCS-V2/article/view/11112
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