Results 1 to 10 of about 1,246,472 (193)
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 +5 more sources
The global k-means clustering algorithm
Pattern Recognition, 2003 We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions.
Aristidis Likas, Nikos Vlassis
exaly +7 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-
Wang X, Bai Y.
europepmc +5 more sources
Genetic K-means algorithm [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999 In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both.
Krishna Kummamuru, M. Murty
semanticscholar +4 more sources
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 +2 more sources
Unsupervised Multi-View K-Means Clustering Algorithm
IEEE Access, 2023 Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
doaj +2 more sources
Image segmentation based on adaptive K-means algorithm
EURASIP Journal on Image and Video Processing, 2018 Image segmentation is an important preprocessing operation in image recognition and computer vision. This paper proposes an adaptive K-means image segmentation method, which generates accurate segmentation results with simple operation and avoids the ...
Xin Zheng +4 more
doaj +2 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 +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
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 +2 more sources