Results 1 to 10 of about 1,986,530 (277)
Adaptive Initialization Method for K-Means Algorithm [PDF]
Frontiers in Artificial Intelligence, 2021 The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local ...
Jie Yang +4 more
doaj +6 more sources
Reducing the time requirement of k-means algorithm. [PDF]
PLoS ONE, 2012 Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d.
Victor Chukwudi Osamor +3 more
doaj +11 more sources
Clustering Using Boosted Constrained k-Means Algorithm [PDF]
Frontiers in Robotics and AI, 2018 This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle.
Masayuki Okabe, Seiji Yamada
doaj +5 more sources
Fast k-means algorithm clustering
International journal of Computer Networks & Communications, 2011 k-means has recently been recognized as one of the best algorithms for clustering unsupervised data. Since k-means depends mainly on distance calculation between all data points and the centers, the time cost will be high when the size of the dataset is ...
Kecman, Vojislav +4 more
core +2 more sources
Unsupervised K-Means Clustering Algorithm [PDF]
IEEE Access, 2020 The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
doaj +2 more sources
An Improved K-Means Algorithm Based on Evidence Distance [PDF]
Entropy, 2021 The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points.
Ailin Zhu +4 more
doaj +2 more sources
Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm. [PDF]
PLoS One, 2016 In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules.
Xu Y, Wu J, Yin CC, Mao Y.
europepmc +8 more sources
The global Minmax k-means algorithm. [PDF]
Springerplus, 2016 The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances.
Wang X, Bai Y.
europepmc +4 more sources
Iris Recognition Using Image Moments and k-Means Algorithm [PDF]
The Scientific World Journal, 2014 This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm.
Yaser Daanial Khan +3 more
doaj +2 more sources
Stable K Multiple-Means Clustering Algorithm
Jisuanji kexue yu tansuo, 2021 For improving the performance of K-means on the nonconvex cluster, a multiple-means clustering method with specified K clusters partitions the original data into multiple subclasses, and formalizes the multiple-means clustering problem as an optimization
ZHANG Nini, GE Hongwei
doaj +1 more source