A fast Apriori algorithm for telecom fraud

Yeqing Yi; Jiyu Shi; Yunru Liu

doi:10.1117/12.3009410

16 October 2023 A fast Apriori algorithm for telecom fraud

Yeqing Yi, Jiyu Shi, Yunru Liu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 1280309 (2023) https://doi.org/10.1117/12.3009410
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Telecom fraud has become a pressing issue, with most research targeting effective countermeasures. However, extracting valuable rules from fraud data to improve case handling efficiency remains under-explored. The Apriori algorithm, commonly used for association rule mining, faces challenges due to its low efficiency and scalability arising from numerous frequent and candidate item sets. To address this problem, we propose a fast Apriori algorithm. The main idea is as follows: First, we establish a similarity measurement model based on information entropy, and combine similar items to significantly reduce the number of frequent item sets and candidate item sets. Second, we optimize the process of generating frequent item sets and candidate item sets and reduce the number of database scans. Third, we apply the improved algorithm to mine the data set of telecom fraud cases in a city, and obtain some meaningful association rules that reflect the relationships among the duration of the crime, the reporting time, and the amount of fraud. These rules provide a new perspective and idea for investigating telecom fraud cases.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yeqing Yi, Jiyu Shi, and Yunru Liu "A fast Apriori algorithm for telecom fraud", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 1280309 (16 October 2023); https://doi.org/10.1117/12.3009410

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