Results 21 to 30 of about 30,970,765 (331)
Stop using the elbow criterion for k-means and how to choose the number of clusters instead [PDF]
SIGKDD Explorations, 2022 A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw poor conclusions from a common heuristic, the "elbow method".
Erich Schubert
semanticscholar +1 more source
Federated K-means Clustering [PDF]
arXiv.org, 2023 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.
Swier Garst, Marcel J. T. Reinders
semanticscholar +1 more source
Improved Guarantees for k-means++ and k-means++ Parallel
CoRR, 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
A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
Applied Sciences, 2022 Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments.
Zekai Cheng +5 more
doaj +1 more source
K-Means and Alternative Clustering Methods in Modern Power Systems
IEEE Access, 2023 As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases.
S. Miraftabzadeh +3 more
semanticscholar +1 more source
, 2022 S k-means program was developed by a team led by Quang-Van DOAN at the Center for Computational Sciences (CCS), the University of Tsukuba. S k-means can be used by any person or entity for any purpose without any fee or charge.
Van Doan
core +1 more source
TSR-YOLO: A Chinese Traffic Sign Recognition Algorithm for Intelligent Vehicles in Complex Scenes
Sensors, 2023 Recognizing traffic signs is an essential component of intelligent driving systems’ environment perception technology. In real-world applications, traffic sign recognition is easily influenced by variables such as light intensity, extreme weather, and ...
Weizhen Song, Shahrel Azmin Suandi
doaj +1 more source
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
Electronics, 2020 The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random ...
Mohiuddin Ahmed +2 more
semanticscholar +1 more source
How to Use K-means for Big Data Clustering? [PDF]
Pattern Recognition, 2022 K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data.
R. Mussabayev +3 more
semanticscholar +1 more source