Results 21 to 30 of about 11,198,889 (292)
$k$-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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CoRR, 2023 Leibniz International Proceedings in Informatics (LIPIcs ...
Grunau, Christoph +2 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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Optimized Cartesian K-Means [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2015 Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two
Jianfeng Wang +5 more
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K-means** - a fast and efficient K-means algorithms
International Journal of Intelligent Information and Database Systems, 2018 K-means often converges to a local optimum. In improved versions of K-means, k-means++ is well-known for achieving a rather optimum solution with its cluster initialisation strategy and high computational efficiency. Incremental K-means is recognised for its converging to the empirically global optimum but having a high complexity due to its stepping ...
Cuong Duc Nguyen, Trong Hai Duong
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CoRR, 2020 This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid distance computation. The Ball k-means can accurately find the neighbor clusters for each cluster resulting distance computations only between a point and its neighbor ...
Shuyin Xia +6 more
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Improved YOLOv5m model based on Swin Transformer, K-means++, and Efficient Intersection over Union (EIoU) loss function for cocoa tree (Theobroma cacao) disease detection [PDF]
Journal of Plant Protection ResearchThe cocoa tree is prone to diverse diseases such as stem borer, stem canker, swollen shot, and root rot disease which impedes high yield. Early disease detection is a critical component of diverse management processes that are implemented throughout the ...
Benedicta Nana Esi Nyarko +3 more
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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
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CoRR, 2022 The $k$-means is one of the most important unsupervised learning techniques in statistics and computer science. The goal is to partition a data set into many clusters, such that observations within clusters are the most homogeneous and observations between clusters are the most heterogeneous.
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