Results 41 to 50 of about 940,524 (345)

Directional clustering through matrix factorization [PDF]

, 2016
This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction.
Blumensath, Thomas
core   +1 more source

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

IOP Conference Series: Materials Science and Engineering, 2018
Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects.
M. Syakur, B. K. Khotimah, E. M. S. Rochman, Budi dwi Satoto   +3 more
semanticscholar   +1 more source

Learning-Augmented $k$-means Clustering

CoRR, 2021
ICLR ...
Jon C. Ergun, Zhili Feng, Sandeep Silwal, David P. Woodruff, Samson Zhou   +4 more
openaire   +3 more sources

Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province

, 2020
Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries.
Sutoyo, Edi, Khairani, Nabila Amalia
core   +1 more source

K-Means Clustering With Incomplete Data

IEEE Access, 2019
Clustering has been intensively studied in machine learning and data mining communities. Although demonstrating promising performance in various applications, most of the existing clustering algorithms cannot efficiently handle clustering tasks with ...
Siwei Wang, Miaomiao Li, Ning Hu, En Zhu, Jingtao Hu, Xinwang Liu, Jianping Yin   +6 more
doaj   +1 more source

SC3s: efficient scaling of single cell consensus clustering to millions of cells

BMC Bioinformatics, 2022
Background Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering.
Fu Xiang Quah, Martin Hemberg
doaj   +1 more source

Selective inference for k-means clustering

Journal of machine learning research : JMLR, 2022
We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we take a selective inference approach.
Yiqun T. Chen, Daniela M. Witten
openaire   +5 more sources

Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm

Big Data and Cognitive Computing, 2019
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human ...
Md. Shahariar Alam, M. Rahman, Mohammad Amazad Hossain, Md. Khairul Islam, Kazi Mowdud Ahmed, K. T. Ahmed, B. C. Singh, M. S. Miah   +7 more
semanticscholar   +1 more source

Design and Implementation of an Improved K-Means Clustering Algorithm

Mobile Information Systems, 2022
Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes ...
Huiling Zhao
semanticscholar   +1 more source

Feature Weighting in k-Means Clustering [PDF]

Machine Learning, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dharmendra S. Modha, W. Scott Spangler
openaire   +1 more source