Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Data Mining Methods: K-Means Clustering Algorithms

International Journal of Cyber and IT Service Management · Vol. 3 No. 1 (2023) · hlm. 40–47

DOI
10.34306/ijcitsm.v3i1.122
ISSN
2808-554X, 2797-1325
Terbit
2023-03-29
Penerbit
Pandawan Sejahtera Indonesia
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0/

Abstrak

A data warehouse is a straightforward definition of a database. Data mining technology can be used to process mountains of data in databases to uncover new, fascinating, and useful information.Clustering is an approach to data gathering. As one technique for grouping data into clusters or groups, the K-Means Clustering Algorithm algorithm divides the data into those that share the cluster's traits and those that don't. data into groups, and data into groups, so that data into groups, and data into groups, so that data has the same traits is grouped in the same cluster. Other clusters are formed from data and clusters with distinct properties. additional categories. The knowledge/information gathered in the groups or clusters is helpful to policy consumers in the decision-making process.mact of making decisions.

Kata kunci

Jaringan kolaborasi para penulis

33 penulis · 85 tautan kepenulisan bersama · 13 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Universiti Teknologi MARA
  • Asian Institute of Management
  • Suan Sunandha Rajabhat University
  • Lincoln University College
  • University of Malaya
  • Skyline University College
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Nusrat AhmedAfiliasi tidak diketahui2
Mugiyo HartonoAfiliasi tidak diketahui2
Albert TanAsian Institute of Management1
Olivier P. RocheAsian Institute of Management1
Md. Uzir Hossain UzirLincoln University College1
Samsul Farid SamsuddinUniversity of Malaya1
Suharti IshakUniversiti Teknologi MARA1
Mohd Rizaimy ShaharudinUniversiti Teknologi MARA1
Wissawa AunyawongSuan Sunandha Rajabhat University1
Natpatsaya SetthachotsombutSuan Sunandha Rajabhat University1
Y. RamakrishnaSkyline University College1
Shariff HarunUniversiti Teknologi MARA1
Azimah DaudUniversiti Teknologi MARA1
Moez MarrouchiAfiliasi tidak diketahui1
Agus RaharjoAfiliasi tidak diketahui1
Martin SudarmonoAfiliasi tidak diketahui1
Anugrah Wahyu NugraAfiliasi tidak diketahui1
Moch Fahmi AbdulazizAfiliasi tidak diketahui1
Rezkya Wahyu AuliaAfiliasi tidak diketahui1
Untung RahardjaAfiliasi tidak diketahui1
Nanda SeptianiAfiliasi tidak diketahui1
Cicilia Sriliasta BangunAfiliasi tidak diketahui1
Tessa HandraAfiliasi tidak diketahui1
Moh. BurhanudinAfiliasi tidak diketahui1
sulaiman MPdAfiliasi tidak diketahui1
Ulil AzmiAfiliasi tidak diketahui1
Hermawan Pamot RaharjoAfiliasi tidak diketahui1
Harry PramonoAfiliasi tidak diketahui1
Dwi Gansar Santi WijayantiAfiliasi tidak diketahui1
Adi SAfiliasi tidak diketahui1
Donny Wira Yudha KusumaAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://iiast.iaic-publisher.org/ijcitsm/index.php/IJCITSM/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Data Mining Methods: K-Means Clustering Algorithms</dc:title>
	<dc:creator xml:lang="en">Annas, Mohammad </dc:creator>
	<dc:creator xml:lang="en">Wahab, Siti Norida </dc:creator>
	<dc:subject xml:lang="en">Data Mining</dc:subject>
	<dc:subject xml:lang="en">Clustering</dc:subject>
	<dc:subject xml:lang="en">K-Means Clustering Algorithm</dc:subject>
	<dc:description xml:lang="en">A data warehouse is a straightforward definition of a database. Data mining technology can be used to process mountains of data in databases to uncover new, fascinating, and useful information.Clustering is an approach to data gathering. As one technique for grouping data into clusters or groups, the K-Means Clustering Algorithm algorithm divides the data into those that share the cluster's traits and those that don't. data into groups, and data into groups, so that data into groups, and data into groups, so that data has the same traits is grouped in the same cluster. Other clusters are formed from data and clusters with distinct properties. additional categories. The knowledge/information gathered in the groups or clusters is helpful to policy consumers in the decision-making process.mact of making decisions.</dc:description>
	<dc:publisher xml:lang="en">Pandawan Sejahtera Indonesia</dc:publisher>
	<dc:date>2023-03-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/122</dc:identifier>
	<dc:identifier>10.34306/ijcitsm.v3i1.122</dc:identifier>
	<dc:source xml:lang="en">International Journal of Cyber and IT Service Management; Vol. 3 No. 1 (2023): April ; 40-47</dc:source>
	<dc:source>2808-554X</dc:source>
	<dc:source>2797-1325</dc:source>
	<dc:source>10.34306/ijcitsm.v3i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://iiast.iaic-publisher.org/ijcitsm/IJCITSM/article/view/122/51</dc:relation>
	<dc:relation>10.34306/ijcitsm.v3i1.122.g51</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Mohammad Annas, Siti Norida Wahab</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0/</dc:rights>
</oai_dc:dc>