Results 31 to 40 of about 30,970,765 (331)
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
doaj +1 more source
On $k$-means for segments and polylines
CoRR, 2023 18 pages, 3 ...
Cabello, S., Giannopoulos, P.
openaire +6 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
Towards optimal lower bounds for k-median and k-means coresets [PDF]
Symposium on the Theory of Computing, 2022 The (k,z)-clustering problem consists of finding a set of k points called centers, such that the sum of distances raised to the power of z of every data point to its closest center is minimized.
Vincent Cohen-Addad +3 more
semanticscholar +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
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
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
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,
Georgios Douzas, F. Bação, F. Last
semanticscholar +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
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
openaire +4 more sources