A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering

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-Me...

Full description

Bibliographic Details
Main Authors: Haviluddin Haviluddin, Muhammad Iqbal, Gubtha Mahendra Putra, Novianti Puspitasari, Hario Jati Setyadi, Felix Andika Dwiyanto, Aji Prasetya Wibawa, Rayner Alfred
Format: Proceedings
Language:English
English
Published: Institute of Electrical and Electronics Engineers 2021
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/31909/1/A%20performance%20comparison%20of%20euclidean%2C%20manhattan%20and%20minkowski%20distances%20in%20k-means%20clustering.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/31909/2/A%20Performance%20Comparison%20of%20Euclidean%2C%20Manhattan%20and%20Minkowski%20Distances%20in%20K-Means%20Clustering.pdf
_version_ 1796911123278069760
author Haviluddin Haviluddin
Muhammad Iqbal
Gubtha Mahendra Putra
Novianti Puspitasari
Hario Jati Setyadi
Felix Andika Dwiyanto
Aji Prasetya Wibawa
Rayner Alfred
author_facet Haviluddin Haviluddin
Muhammad Iqbal
Gubtha Mahendra Putra
Novianti Puspitasari
Hario Jati Setyadi
Felix Andika Dwiyanto
Aji Prasetya Wibawa
Rayner Alfred
author_sort Haviluddin Haviluddin
collection UMS
description 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.
first_indexed 2024-03-06T03:14:09Z
format Proceedings
id ums.eprints-31909
institution Universiti Malaysia Sabah
language English
English
last_indexed 2024-03-06T03:14:09Z
publishDate 2021
publisher Institute of Electrical and Electronics Engineers
record_format dspace
spelling ums.eprints-319092022-03-18T05:28:15Z https://eprints.ums.edu.my/id/eprint/31909/ A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering Haviluddin Haviluddin Muhammad Iqbal Gubtha Mahendra Putra Novianti Puspitasari Hario Jati Setyadi Felix Andika Dwiyanto Aji Prasetya Wibawa Rayner Alfred DS611-649 Indonesia (Dutch East Indies) QA1-43 General 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. Institute of Electrical and Electronics Engineers 2021-04-05 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/31909/1/A%20performance%20comparison%20of%20euclidean%2C%20manhattan%20and%20minkowski%20distances%20in%20k-means%20clustering.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/31909/2/A%20Performance%20Comparison%20of%20Euclidean%2C%20Manhattan%20and%20Minkowski%20Distances%20in%20K-Means%20Clustering.pdf Haviluddin Haviluddin and Muhammad Iqbal and Gubtha Mahendra Putra and Novianti Puspitasari and Hario Jati Setyadi and Felix Andika Dwiyanto and Aji Prasetya Wibawa and Rayner Alfred (2021) A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering. https://ieeexplore.ieee.org/document/9392053
spellingShingle DS611-649 Indonesia (Dutch East Indies)
QA1-43 General
Haviluddin Haviluddin
Muhammad Iqbal
Gubtha Mahendra Putra
Novianti Puspitasari
Hario Jati Setyadi
Felix Andika Dwiyanto
Aji Prasetya Wibawa
Rayner Alfred
A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
title A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
title_full A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
title_fullStr A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
title_full_unstemmed A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
title_short A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
title_sort performance comparison of euclidean manhattan and minkowski distances in k means clustering
topic DS611-649 Indonesia (Dutch East Indies)
QA1-43 General
url https://eprints.ums.edu.my/id/eprint/31909/1/A%20performance%20comparison%20of%20euclidean%2C%20manhattan%20and%20minkowski%20distances%20in%20k-means%20clustering.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/31909/2/A%20Performance%20Comparison%20of%20Euclidean%2C%20Manhattan%20and%20Minkowski%20Distances%20in%20K-Means%20Clustering.pdf
work_keys_str_mv AT haviluddinhaviluddin aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT muhammadiqbal aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT gubthamahendraputra aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT noviantipuspitasari aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT hariojatisetyadi aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT felixandikadwiyanto aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT ajiprasetyawibawa aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT rayneralfred aperformancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT haviluddinhaviluddin performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT muhammadiqbal performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT gubthamahendraputra performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT noviantipuspitasari performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT hariojatisetyadi performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT felixandikadwiyanto performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT ajiprasetyawibawa performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering
AT rayneralfred performancecomparisonofeuclideanmanhattanandminkowskidistancesinkmeansclustering