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
相关论文
共 50 条
  • [1] Proportional Data Clustering using K-Means Algorithm: A comparison of different distances
    Singh, Jai Puneet
    Bouguila, Nizar
    2017 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2017, : 1048 - 1052
  • [2] Comparison of conventional and rough K-means clustering
    Lingras, P
    Yan, R
    West, C
    ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, 2003, 2639 : 130 - 137
  • [3] Performance evaluation of K-means clustering on Hadoop infrastructure
    Vats, Satvik
    Sagar, B. B.
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2019, 22 (08): : 1349 - 1363
  • [4] Improving Clustering Method Performance Using K-Means, Mini Batch K-Means, BIRCH and Spectral
    Wahyuningrum, Tenia
    Khomsah, Siti
    Suyanto, Suyanto
    Meliana, Selly
    Yunanto, Prasti Eko
    Al Maki, Wikky F.
    2021 4TH INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS (ISRITI 2021), 2020,
  • [5] Transformed K-means Clustering
    Goel, Anurag
    Majumdar, Angshul
    29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021), 2021, : 1526 - 1530
  • [6] Clustering performance comparison using K-means and expectation maximization algorithms
    Jung, Yong Gyu
    Kang, Min Soo
    Heo, Jun
    BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT, 2014, 28 : S44 - S48
  • [7] Deep k-Means: Jointly clustering with k-Means and learning representations
    Fard, Maziar Moradi
    Thonet, Thibaut
    Gaussier, Eric
    PATTERN RECOGNITION LETTERS, 2020, 138 : 185 - 192
  • [8] Comparison of K-means and GMM methods for contextual clustering in HSM
    Wang, Zhiqiang
    Da Cunha, Catherine
    Ritou, Mathieu
    Furet, Benoit
    7TH INTERNATIONAL CONFERENCE ON CHANGEABLE, AGILE, RECONFIGURABLE AND VIRTUAL PRODUCTION (CARV2018), 2019, 28 : 154 - 159
  • [9] Clustering Performance of an Evolutionary K-Means Algorithm
    Nigro, Libero
    Cicirelli, Franco
    Pupo, Francesco
    PROCEEDINGS OF NINTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, VOL 9, ICICT 2024, 2025, 1054 : 359 - 369
  • [10] Statistically Improving K-means Clustering Performance
    Ihsanoglu, Abdullah
    Zaval, Mounes
    32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024, 2024,
12345