Results 21 to 30 of about 14,276,290 (299)
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
, 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
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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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
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
openaire +2 more sources
, 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
core +1 more source
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
doaj +1 more source
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
openaire +6 more sources
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