2021
DOI: 10.1155/2021/9446653
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k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Abstract: At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This artic… Show more

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Cited by 19 publications
(11 citation statements)
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“…Chili leaf Disease identification (R2 value ≤ 0.99) [55] Optimal thresholding Chili leaf Disease identification ≤ 94% [56] A k-means clustering approach is an unsupervised form of machine learning used to segment ROI, such as those derived from images of crop leaves. Since it is suited for data sets with enormous quantities of data and high feature dimensions, as well as having a low dependency on the data itself, the k-means clustering technique has become one of the most used approaches for segmentation [57]. The authors in [58] entail the usage of k-means clustering on crop leaves with several steps.…”
Section: Image Segmentationmentioning
confidence: 99%
“…Chili leaf Disease identification (R2 value ≤ 0.99) [55] Optimal thresholding Chili leaf Disease identification ≤ 94% [56] A k-means clustering approach is an unsupervised form of machine learning used to segment ROI, such as those derived from images of crop leaves. Since it is suited for data sets with enormous quantities of data and high feature dimensions, as well as having a low dependency on the data itself, the k-means clustering technique has become one of the most used approaches for segmentation [57]. The authors in [58] entail the usage of k-means clustering on crop leaves with several steps.…”
Section: Image Segmentationmentioning
confidence: 99%
“…Menurut [10] [11] [12], proses K Means Clustering pada d dimensi memiliki proses yang sama dengan high dimensional data, tetapi pada high dimensional data membutuhkan jumlah perulangan sejumlah dimensi data. Pada penelitian ini data gambar terdiri dari 3 dimensi yaitu red dimensi, green dimensi, blue dimensi diubah menjadi dua dimensi yaitu berwarna abu-abu.…”
Section: Inputunclassified
“…K-means clustering is an algorithm for classifying or grouping objects based on attributes into a number of K groups (positive integers number). The clustering is done by minimizing the sum of the squares of the distance between the data and the corresponding cluster centroid [14], [15]. This K-means clustering method partitions data into groups so that data with the same characteristics are included in the same group and data with different characteristics are grouped into other groups [16], [17].…”
Section: K-means Clusteringmentioning
confidence: 99%