Integration k-means clustering method and elbow method for identification of the best customer profile cluster
Clustering is a data mining technique used to analyse data that has variations and the
number of lots. Clustering was process of grou** data into a cluster, so they contained …
number of lots. Clustering was process of grou** data into a cluster, so they contained …
Effect of distance metrics in determining k-value in k-means clustering using elbow and silhouette method
DM Saputra, D Saputra, LD Oswari - … international conference on …, 2020 - atlantis-press.com
Clustering is one of the main task in datamining. It is useful to group and cluster the data.
There are a few ways to cluster the data such as partitional-based, hierarchical-based and …
There are a few ways to cluster the data such as partitional-based, hierarchical-based and …
A comparative study of software defect binomial classification prediction models based on machine learning
H Tao, X Niu, L Xu, L Fu, Q Cao, H Chen… - Software Quality …, 2024 - Springer
As information technology continues to advance, software applications are becoming
increasingly critical. However, the growing size and complexity of software development can …
increasingly critical. However, the growing size and complexity of software development can …
Severity detection of COVID-19 infection with machine learning of clinical records and CT images
F Zhu, Z Zhu, Y Zhang, H Zhu, Z Gao… - … and Health Care, 2022 - content.iospress.com
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading
rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may …
rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may …
Detection and diagnosis of process fault using unsupervised learning methods and unlabeled data
Supervised learning methods, commonly used for process monitoring, require labeled
historical datasets for normal condition as well for each faulty condition, which demands …
historical datasets for normal condition as well for each faulty condition, which demands …
Pendekatan Initial Centroid Search Untuk Meningkatkan Efisiensi Iterasi Klustering K-Means.
Pengelompokan K-Means bertujuan untuk mengumpulkan satu set titik pusat cluster yang
optimal melalui iterasi yang berurutan. Fakta bahwa semakin optimal posisi dari titik pusat …
optimal melalui iterasi yang berurutan. Fakta bahwa semakin optimal posisi dari titik pusat …
Perbandingan metode K-Means dengan fuzzy C-Means untuk analisa karakteristik mahasiswa berdasarkan kunjungan ke perpustakaan (Studi kasus Sekolah Tinggi …
NPE Merliana - 2015 - e-journal.uajy.ac.id
Kurangnya minat membaca pada mahasiswa khususnya menjadi perhatian bagi lembaga
pendidikan, karena membaca dapat menambah wawasan, pengetahuan dan informasi …
pendidikan, karena membaca dapat menambah wawasan, pengetahuan dan informasi …
Importance of initialization in K-means clustering
A Gupta, A Tomer, S Dahiya - 2022 Second International …, 2022 - ieeexplore.ieee.org
Data clustering is a method of visualizing the data in such a way that enables the researcher
to see similar patterns formed in the data and these lead to conclusions that can be helpful …
to see similar patterns formed in the data and these lead to conclusions that can be helpful …
Penerapan k-means clustering dari log data moodle untuk menentukan perilaku peserta pada pembelajaran daring
E Ikhsan - Sistemasi: Jurnal Sistem Informasi, 2021 - sistemasi.ftik.unisi.ac.id
Pembelajaran berbasis daring atau e-learning mulai semakin banyak digunakan oleh para
pengampu pelajaran melalui Learning Management System (LMS). Moodle sebagai LMS …
pengampu pelajaran melalui Learning Management System (LMS). Moodle sebagai LMS …
Seismotectonic zoning by K-means clustering analysis in the Korean Peninsula
김성균, 전정수, 전명순 - 지질학회지, 2017 - dbpia.co.kr
지진활동이 활발하지 않은 판내부 지역에서는 확률론적 지진재해도 분석에 사용되는 지진원의
설정이 용이하지 않다. 지진원 모델을 설정하고 평가하는 유일무이한 공식적인 방법은 …
설정이 용이하지 않다. 지진원 모델을 설정하고 평가하는 유일무이한 공식적인 방법은 …