Results 261 to 270 of about 14,276,290 (299)

K-means clustering applied to vegetation indices for mapping cultivated areas using high-resolution Moroccan Mohammed VI satellite imagery. [PDF]

Sci Rep
Moussaid A, Bayad M, Gamoussi Y, El Hamdouni Y, Briak H.   +4 more
europepmc   +1 more source

Automatic modulation classification method using fixed K-means algorithm for feature clustering processing. [PDF]

PLoS One
Yuan L, Chen Y.
europepmc   +1 more source

Phenotypic Classification of Scalp High-Frequency Oscillations in Absence Epilepsy Based on Multiple Characteristics Using K-Means Clustering. [PDF]

Bioengineering (Basel)
Maeda K, Tsuboi H, Hosoda N, Fukumoto J, Fujita S, Yamaguchi S, Ichino N, Osakabe K, Sugimoto K, Furukawa G, Ishihara N.   +10 more
europepmc   +1 more source

Healthcare utilization and chronic condition clusters in multimorbidity patients using weighted k-means: a register-based study in Denmark. [PDF]

Health Inf Sci Syst
Anthonimuthu DJ, Holm NN, Stockmarr A, Zwisler AD, Udsen FW.   +4 more
europepmc   +1 more source

Exploring homology detection via k-means clustering of proteins embedded with a large language model. [PDF]

Bioinformatics
Minotto T, Claessens A, Otto TD.
europepmc   +1 more source

k -means: A revisit

Neurocomputing, 2018
Abstract Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago. The performance of k-means has been enhanced from different perspectives over the years. Unfortunately, a good trade-off between quality and efficiency is hardly reached. In this paper, a novel k-means variant is presented.
Wan-Lei Zhao, Chong Wah Ngo
exaly   +5 more sources

Some of the next articles are maybe not open access.

Related searches:

Cartesian K-Means

2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity ...
Mohammad Norouzi 0002, David J. Fleet
openaire   +1 more source

K*-Means: An Effective and Efficient K-Means Clustering Algorithm

2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), 2016
K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering.
Jianpeng Qi, Yanwei Yu, Lihong Wang, Jinglei Liu   +3 more
openaire   +2 more sources

Balanced k-Means

2017
K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
openaire   +2 more sources

PERFORMANCE COMPARISON OF K-MEANS, PARALLEL K-MEANS AND K-MEANS++

K-means clustering is a fundamental unsupervised machine learning technique widely applied in various domains such as data analysis, pattern recognition, and clustering-based tasks. However, its efficiency and scalability can be challenged, particularly when dealing with large-scale datasets and complex data structures.
Aliguliyev, Ramiz, Shalala F. Tahirzada
openaire   +1 more source