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Dynamic load balancing in parallel KD-tree k-means [PDF]
, 2010 One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions.
Di Fatta, Giuseppe, Pettinger, David
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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
Penggunaan N-mers Frequency pada Analisis Barisan DNA
Jambura Journal of Mathematics, 2020 Salah satu metode untuk menganalisis barisan DNA adalah menggunaan N-mers Frequency. N-mers Frequency termasuk metode data mining pada barisan DNA, dimana barisan DNA yang merupakan data string “ACGT” akan diubah menjadi data numerik.
Khoirul Umam, Rahmat Sagara
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Journal of Statistical Computation and Simulation, 2017 Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random. Randomized initialization with uneven weights ({\tt k-means++}) has recently been used to improve the performance over this strategy in cost and run-time.
Yoder, Jordan, Priebe, Carey E.
openaire +2 more sources
Faster K-Means Cluster Estimation
, 2017 There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for ...
A Likas, DT Pham, SP Lloyd, T Kanungo
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Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images
Remote Sensing, 2020 All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for
Lukáš Krauz +3 more
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The Monongahela tradition in "real time": Bayesian analysis of radiocarbon dates.
PLoS ONE, 2022 Despite advances in techniques, methods, and theory, northeastern North American archaeologists continue to use early to mid-twentieth century culture historical taxa as units of analysis and narrative.
John P Hart, Bernard K Means
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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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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
semanticscholar +1 more source