Results 1 to 10 of about 1,834,473 (165)

K-Means Cloning: Adaptive Spherical K-Means Clustering [PDF]

open access: yesAlgorithms, 2018
We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters.
Abdel-Rahman Hedar   +3 more
doaj   +3 more sources

Unsupervised K-Means Clustering Algorithm

open access: yesIEEE Access, 2020
The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
doaj   +3 more sources

Causal K-Means Clustering. [PDF]

open access: yesJ R Stat Soc Series B Stat Methodol
Abstract Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgroups. Since the subgroup structure is unknown, identifying and evaluating subgroup specific effects is substantially more challenging than standard population level analysis.
Kim K, Kim J, Kennedy EH.
europepmc   +3 more sources

k -means: A revisit

open access: yesNeurocomputing, 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   +4 more sources

t-k-means: A ROBUST AND STABLE k-means VARIANT [PDF]

open access: yesICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of the Gaussian distribution, $k$-means algorithm suffers from relatively poor performance on the dataset containing heavy-tailed data or outliers.
Yiming Li 0004   +5 more
openaire   +2 more sources

Exact Acceleration of K-Means++ and K-Means|| [PDF]

open access: yesProceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021
K-Means++ and its distributed variant K-Means|| have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation,and theoretical grounding of the K-means++ and || methods have made them difficult to "best" from a holistic perspective.
openaire   +2 more sources

SOFT CLUSTERING DENGAN ALGORITMA FUZZY K-MEANS (STUDI KASUS : PENGELOMPOKAN DESA DI KOTA TIDORE KEPULAUAN)

open access: yesBarekeng, 2021
Mengembangkan wilayah untuk mengurangi kesenjangan dan menjamin pemerataan merupakan salah satu dari tujuh agenda Pembangunana RPJMN IV Tahun 2020-2024. Setiap wilayah tentunya memiliki potensi yang berbeda, baik potensi fisik maupun non-fisik. Perbedaan
Muhamad Budiman Johra
doaj   +1 more source

Spatiotemporal k-means

open access: yesCoRR, 2022
18 pages, 5 ...
Dorabiala, Olga   +4 more
openaire   +2 more sources

Improved Guarantees for k-means++ and k-means++ Parallel

open access: yesCoRR, 2020
In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved approximation and bi-criteria approximation guarantees for k-means++ and k-means++ parallel.
Konstantin Makarychev   +2 more
openaire   +3 more sources

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