A Performance Comparison of Euclidean, Manhattan and Minkowski Distances in K-Means Clustering

被引：0
|
作者：
Haviluddin [1 ]
Iqbal, Muhammad [1 ]
Putra, Gubtha Mahendra [1 ]
Puspitasari, Novianti [1 ]
Setyadi, Hario Jati [1 ]
Dwiyanto, Felix Andika [2 ]
Wibawa, Aji Prasetya [3 ]
Alfred, Rayner [4 ]
机构：
[1] Univ Mulawarman, Dept Informat, Samarinda, Indonesia
[2] Univ Negeri Malang, Grad Sch, Malang, Indonesia
[3] Univ Negeri Malang, Dept Elect Engn, Malang, Indonesia
[4] Univ Malaysia Sabah, Fac Comp & Informat, Kota Kinabalu, Sabah, Malaysia
来源：
2020 6TH INTERNATIONAL CONFERENCE ON SCIENCE IN INFORMATION TECHNOLOGY (ICSITECH): EMBRACING INDUSTRY 4.0: TOWARDS INNOVATION IN DISASTER MANAGEMENT | 2020年
关键词：
crime; clustering; K-Means; Euclidian distance; Manhattan distance; Minkowski distance; SSE;
D O I：
10.1109/ICSITech49800.2020.9392053
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Indonesian police department has a role in maintaining security and law enforcement under the Republic of Indonesia Law Number 2 of 2002. In this study, data on the crime rate in the Bontang City area has been analyzed. It becomes the basis for the Police in anticipating various crimes. The K-Means algorithm is used for data analysis. Based on the test results, there are three levels of crime: high, medium, and low. According to the analysis, the high crime rate in the Bontang City area is special case theft and vehicle theft. Furthermore, it becomes the police program to maintain personal and vehicle safety.
引用
收藏
页码：184 / 188
页数：5
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