An efficient Apriori algorithm for frequent pattern in human intoxication data

被引：10

作者：

Hassan, Md. Mehedi ^{[1
]}

Zaman, Sadika ^{[1
]}

Mollick, Swarnali ^{[2
]}

Hassan, Md. Mahedi ^{[3
]}

Raihan, M. ^{[1
]}

Kaushal, Chetna ^{[4
]}

Bhardwaj, Rajat ^{[5
]}

机构：

[1] North Western Univ, Comp Sci & Engn, Khulna, Bangladesh

[2] Northern Univ Business & Technol, Comp Sci & Engn, Khulna, Bangladesh

[3] Bangladesh Univ Business & Technol, Comp Sci & Engn, Dhaka, Bangladesh

[4] Chitkara Univ, Inst Engn & Technol, Rajpura, Punjab, India

[5] Deemed to be Univ Jain, Dept Comp Sci & Engn, Bengaluru, India

来源：

INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING | 2023年 / 19卷 / 01期

关键词：

Machine learning; Drug; Addiction; Apriori; Association rule mining; ARTIFICIAL NEURAL-NETWORK;

D O I：

10.1007/s11334-022-00523-w

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Addicts and non-addicts can be distinguished by analyzing social behaviors and activities. An attempt was made in the study to discover the main rules that cause people to get hooked. We utilized an open-source dataset with 474 total instances and 212 total addicted individuals. They asked 50 questions during the data collection process. All of the questions were created using the Index of Addiction Severity and with the assistance of drug addiction psychologists. In this study, we utilized the Apriori algorithm to extract the most important rules from the dataset. By following this guideline, it will be clear whether or not someone is hooked based on their social conduct. The Apriori algorithm was used to find rules from the dataset, and eight significant rules were discovered, with a confidence level of 95% and a support level of 45%.

引用

页码：61 / 69

页数：9

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