Results 61 to 70 of about 17,769,086 (225)
Soil data clustering by using K-means and fuzzy K-means algorithm
Telfor Journal, 2016 A problem of soil clustering based on the chemical characteristics of soil, and proper visual representation of the obtained results, is analysed in the paper. To that aim, K-means and fuzzy K-means algorithms are adapted for soil data clustering.
E. Hot, V. Popović-Bugarin
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K-MEANS WITH SAMPLING FOR DETERMINING PROMINENT COLORS IN IMAGES
ICTACT Journal on Soft Computing, 2022 A tool that quickly calculates the dominant colors of an image can be very useful in image processing. The k-means clustering algorithm has this potential since it partitions a set of data into n clusters and returns a representative data point from each
Angelina Cheng +2 more
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Binning-Based Silhouette Approach to Find the Optimal Cluster Using K-Means
IEEE Access, 2022 Clustering is one of the critical parts of machine learning algorithms. K-Means clustering is the standard technique that various data analysts use for clustering the data among the various clusters.
Akash Punhani +3 more
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Fast k-means algorithm clustering
, 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
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Clustering with Spectral Norm and the k-means Algorithm [PDF]
, 2010 There has been much progress on efficient algorithms for clustering data points generated by a mixture of $k$ probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of ...
Kannan, Ravindran, Kumar, Amit
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A Feature-Reduction Multi-View k-Means Clustering Algorithm
IEEE Access, 2019 The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas.
Miin-Shen Yang, Kristina P. Sinaga
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New bounds for $k$-means and information $k$-means
, 2021 In this paper, we derive a new dimension-free non-asymptotic upper bound for the quadratic $k$-means excess risk related to the quantization of an i.i.d sample in a separable Hilbert space. We improve the bound of order $\mathcal{O} \bigl( k / \sqrt{n} \bigr)$ of Biau, Devroye and Lugosi, recovering the rate $\sqrt{k/n}$ that has already been proved by
Appert, Gautier, Catoni, Olivier
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Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning
IEEE Access, 2021 Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during the growth and production stages of corn. In order to address this problem, this paper proposes a method based on K-means clustering and an improved deep learning ...
Helong Yu +7 more
semanticscholar +1 more source
PCA and K-Means decipher genome
, 2008 In this paper, we aim to give a tutorial for undergraduate students studying statistical methods and/or bioinformatics. The students will learn how data visualization can help in genomic sequence analysis.
A Zinovyev +8 more
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, 2019 We study the problem of online clustering where a clustering algorithm has to assign a new point that arrives to one of $k$ clusters. The specific formulation we use is the $k$-means objective: At each time step the algorithm has to maintain a set of k candidate centers and the loss incurred is the squared distance between the new point and the closest
Cohen-Addad, Vincent +3 more
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