<i>k</i>‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Wu, Chunqiong; Yan, Bingwen; Yu, Rongrui; Yu, Baoqin; Zhou, Xiukao; Yu, Yanliang; Chen, Na

doi:10.1155/2021/9446653

Cited by 18 publications

(11 citation statements)

References 38 publications

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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%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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Chili, an important crop whosefruit is used as a spice, is significantly hampered by the existence of chili diseases.While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 researchpaperpublications on theidentificationof chilidiseases, with publication dates spanning from 2013 to 2021, arereviewed in this paper.Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.

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Section: Image Segmentationmentioning

confidence: 99%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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“…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

Image Searching Data Gambar Berwarna Algoritma K Means Clustering Pada Data Penjualan

Hayati

2022

SKANIKA

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Pencarian data dengan data gambar berwarna yang diterapkan pada penelitian ini adalah data penjualan dengan dataset gambar berwarna. Terdapat beberapa produk yang akan dideskripsikan yaitu produk makanan, produk bahan pokok, produk rumah tangga yang akan dikelompokkan berdasarkan kategori kelompok dengan kategori produk yang sama. Metode yang diterapkan adalah K Means Clustering pada penelitian ini dengan data pembelajaran dan data uji coba yang berbeda. Sistem pencarian gambar dengan memilih file gambar dari data uji coba dan sistem akan mencari kelompok dari data pembelajaran dan ditampilkan gambar hasil pencarian untuk mempermudah pengguna aplikasi mencari data penjualan jika tidak memiliki atau kesulitan mencari informasi nama produk atau kode produk. Proses pengolahan data warna baik data gambar berwarna pada data pembelajaran dan data uji coba harus melalui proses normalisasi data warna dimana proses ini meliputi pemilihan objek, pemotongan objek, dan menyamakan ukuran gambar objek. Hasil pembelajaran pengelompokan K Means Clustering mendapatkan hasil yang baik, pada penelitian ini mendapatkan prosentase 93,33% pada data pembelajaran dan 60% pada data uji coba.

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“…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%

Optimal PV Placement to Reduce Power Loss and Improve Voltage in Distribution Network System Using K-means Clustering Method

Yunus

Latif²,

Darwison³

2022

AJEEET

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Placing the PV in the right location will maintain the utility voltage, but if the placement of PV in the wrong location will cause the stability of the system to be affected. In this study, optimization of PV placement uses the K-means Clustering method. This method will group each node in the system from the point of view of operating characteristics LSF (loss sensitivity factor) and dV (voltage deviation). The results of grouping each bus with the K-means Clustering method will be the basis for determining the location of PV placement in the IEEE 37 and 69 bus distribution systems. In this method, grouping results are used based on the size of the proximity and have the same characteristics with each other. In determining the optimal location for PV placement, the addition of PV will reduce power losses and improve voltage. Optimal PV location placement in the IEEE 37 bus distribution system is placed on 3 buses with a power capacity of 60% where the value of power losses drops to 176.2 kW and the voltage profile is the best but there are some buses that are still under voltage and overvoltage. Meanwhile, the most optimal PV location for the IEEE 69 bus distribution system is placed on a 6 bus with a power capacity of 60% where the value of power losses drops to 149.5 kW and the voltage profile of each bus is in normal condition..

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k‐Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Cited by 18 publications

References 38 publications

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Image Searching Data Gambar Berwarna Algoritma K Means Clustering Pada Data Penjualan

Optimal PV Placement to Reduce Power Loss and Improve Voltage in Distribution Network System Using K-means Clustering Method

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