Results 11 to 20 of about 18,390,026 (306)
Unsupervised K-Means Clustering Algorithm
IEEE 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
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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
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
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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
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High-Performance Lightweight Fall Detection with an Improved YOLOv5s Algorithm
Machines, 2023 The aging population has drastically increased in the past two decades, stimulating the development of devices for healthcare and medical purposes. As one of the leading potential risks, the injuries caused by accidental falls at home are hazardous to ...
Yuanpeng Wang +4 more
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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
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Oversampling for Imbalanced Learning Based on K-Means and SMOTE [PDF]
Information Sciences, 2017 Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
F. Last, Georgios Douzas, F. Bação
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Unsupervised Multi-View K-Means Clustering Algorithm
IEEE Access, 2023 Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
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Algorithms, 2020 Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces
Joonas Hämäläinen +2 more
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Proceedings of the VLDB Endowment, 2012 Over half a century old and showing no signs of aging, k -means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k -means is crucial for obtaining a good final solution.
Bahmani, Bahman +4 more
openaire +2 more sources