Results 61 to 70 of about 942,452 (277)

k-Means+++: Outliers-Resistant Clustering

Algorithms, 2020
The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a
Adiel Statman, Liat Rozenberg, Dan Feldman   +2 more
doaj   +1 more source

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

Applied Sciences, 2022
With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating ...
Yiping Li, Xiangbing Zhou, Jiangang Gu, Ke Guo, Wu Deng   +4 more
doaj   +1 more source

Performance characterization of clustering algorithms for colour image segmentation [PDF]

, 2006
This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation ...
Ghita, Ovidiu, Ilea, Dana E., Whelan, Paul F.   +2 more
core  

Distributed Kernel K-Means for Large Scale Clustering

, 2017
Clustering samples according to an effective metric and/or vector space representation is a challenging unsupervised learning task with a wide spectrum of applications.
Decherchi, Sergio, Ferrarotti, Marco Jacopo, Rocchia, Walter   +2 more
core   +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

The (Glg)ABCs of cyanobacteria: modelling of glycogen synthesis and functional divergence of glycogen synthases in Synechocystis sp. PCC 6803

FEBS Letters, EarlyView.
We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee, Dimitrios Bekiari, Sofia Doello, Karl Forchhammer   +3 more
wiley   +1 more source

Federated K-Means Clustering

Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown substantially over the last years, unsupervised FL methods remain scarce.
Swier Garst, Marcel J. T. Reinders
openaire   +2 more sources

Structural biology of ferritin nanocages

FEBS Letters, EarlyView.
Ferritin is a conserved iron‐storage protein that sequesters iron as a ferric mineral core within a nanocage, protecting cells from oxidative damage and maintaining iron homeostasis. This review discusses ferritin biology, structure, and function, and highlights recent cryo‐EM studies revealing mechanisms of ferritinophagy, cellular iron uptake, and ...
Eloise Mastrangelo, Flavio Di Pisa
wiley   +1 more source

Spherical k-Means Clustering

Journal of Statistical Software, 2012
Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency.
Kurt Hornik, Ingo Feinerer, Martin Kober, Christian Buchta   +3 more
doaj  

Sparse Multi-View K-Means Clustering

IEEE Access
In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters.
Miin-Shen Yang, Shazia Parveen
doaj   +1 more source