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Metode Elbow dalam Optimasi Jumlah Cluster pada K-Means Clustering

Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 2023
K-Means clustering merupakan salah satu strategi yang digunakan dalam analisis data dan machine learning untuk mengelompokkan data menjadi beberapa kelompok (cluster) berdasarkan kemiripan fitur atau atributnya.
Nadia Annisa Maori, Evanita Evanita
doaj   +1 more source

Improving Scalable K-Means++

Algorithms, 2020
Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces
Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi   +2 more
doaj   +1 more source

Penerapan Algoritma K-Means Untuk Mengklasifikasi Data Obat

Jurnal Sisfokom, 2023
Pengklasifikasian data obat pada sebuah instansi yang bergerak pada bidang Kesehatan merupakan hal yang sangat penting. Kegiatan tersebut tidak lepas dari pengawasan serta monitoring setiap harinya karena pengolahan data obat termasuk inti dalam ...
Ferdy Pangestu Ferdy Pangestu, Nur Yasin Nur Yasin, Ronald Chistover Ronald Chistover Hasugian, yunita yunita   +3 more
doaj   +1 more source

Balanced K-Means for Clustering [PDF]

, 2014
We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm.
Malinen Mikko, Fränti Pasi
openaire   +1 more source

RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm

Journal of Statistical Software, 2016
Witten and Tibshirani (2010) proposed an algorithim to simultaneously find clusters and select clustering variables, called sparse K-means (SK-means).
Yumi Kondo, Matias Salibian-Barrera, Ruben Zamar   +2 more
doaj   +1 more source

K-MEANS WITH SAMPLING FOR DETERMINING PROMINENT COLORS IN IMAGES

ICTACT Journal on Soft Computing, 2022
A tool that quickly calculates the dominant colors of an image can be very useful in image processing. The k-means clustering algorithm has this potential since it partitions a set of data into n clusters and returns a representative data point from each
Angelina Cheng, Eric Rosenberg, Alina Gorbunova   +2 more
doaj   +1 more source

Online k-means Clustering

CoRR, 2019
11 pages, 1 ...
Cohen-Addad, Vincent, Guedj, Benjamin, Kanade, Varun, Rom, Guy   +3 more
openaire   +4 more sources

Clustering Productive Palm Land using the K- Means Clustering Algorithm

Journal of Innovation Information Technology and Application, 2023
Indonesia is a country with a tropical climate that has many oil palm plantations. CV. Alkema Deo is one of the companies that manage oil palm plantations in Sampit City, East Kotawaringin Regency, Central Kalimantan. CV.
Geofanny Widianto Sihite, Eka Prasetyaningrum   +1 more
doaj   +1 more source

Technology Topic Mining and Trend Analysis from the Perspective of Industrial Chain Combined with K-Means and LDA——Taking Virtual Reality Technology as an Example

Zhishi guanli luntan, 2020
[Purpose/significance] From the perspective of industry chain, this paper takes virtual reality technology as an example, constructs VR patent industry chain corpus, and explores the technical theme, research and development hotspot and future ...
Chen Ling, Lin Ping, Duan Yaoqing
doaj   +1 more source

Discriminative k-means clustering [PDF]

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts
openaire   +2 more sources