Results 11 to 20 of about 15,852,135 (305)
, 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
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, 2023 This paper presents two novel deterministic initialization procedures for k-means clustering based on a modified crowding distance. The procedures, named ck-means and fck-means, use more crowded points as initial centroids.
Abdesslem Layeb
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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
doaj +1 more source
K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province
Desimal, 2022 The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district.
Ummu Wachidatul Latifah +2 more
doaj +1 more source
On $k$-means for segments and polylines
CoRR, 2023 18 pages, 3 ...
Cabello, S., Giannopoulos, P.
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The development of a GIS for New Deal Archaeology
Bulletin of the History of Archaeology, 2011 I have recently launched an effort to create a GIS of all New Deal-funded archaeological investigations conducted in the 48 states that comprised the USA during the Great Depression (Means 2011).
Bernard K. Means
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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. The recently proposed
Bahman Bahmani +4 more
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Bulletin of the History of Archaeology, 2015 New Deal archaeology survey and excavation projects across the lower 48 states exhibit considerable geographic variation in their nature and extent. Part of this variation can be linked to strong regional personalities, while other variation depended on ...
Bernard K. Means
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Kernel Probabilistic K-Means Clustering
Sensors, 2021 Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian ...
Bowen Liu +4 more
doaj +1 more source
$k$-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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