Results 11 to 20 of about 1,835,018 (264)
K-Means Cloning: Adaptive Spherical K-Means Clustering [PDF]
Algorithms, 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
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t-k-means: A ROBUST AND STABLE k-means VARIANT [PDF]
ICASSP 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
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Exact Acceleration of K-Means++ and K-Means|| [PDF]
Proceedings 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.
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Causal K-Means Clustering. [PDF]
J R Stat Soc Series B Stat MethodolAbstract 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
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
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On $k$-means for segments and polylines
CoRR, 2023 18 pages, 3 ...
Cabello, S., Giannopoulos, P.
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CoRR, 2023 Leibniz International Proceedings in Informatics (LIPIcs ...
Grunau, Christoph +2 more
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$k$-means clustering of extremes [PDF]
Electronic Journal of Statistics, 2020 The $k$-means clustering algorithm and its variant, the spherical $k$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $k$-means algorithm can be applied in the analysis of only the extremal observations from a data set.
Janßen, Anja, Wan, Phyllis
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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.
Bahman Bahmani +4 more
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