Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model

Patel, Eva; Kushwaha, Dharmender Singh

doi:10.1016/j.procs.2020.04.017

Cited by 123 publications

(79 citation statements)

References 13 publications

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“…Penelitian internasional juga membahas implementasi K-Means Clustering mengelompokkan beban kerja cloud menggunakan K-Means dengan campuran model Gaussian [9]. Ada juga membahas tentang seberapa banyak K-Means dapat ditingkatkan dengan menggunakan inisialisasi dan pengulangan yang lebih baik [10].…”

Section: Pendahuluanunclassified

Tingkat Efisiensi Penggunaan Resep Dokter Spesialis Menggunakan Metode K-Means Clustering

sharon

Defit

Nurcahyo

2021

jidt

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The National Formulary (Fornas) is a list of drugs stipulated in a Decree of the Minister of Health of the Republic of Indonesia, which is used as a guideline for hospitals in drug supply for participants of the National Health Insurance (JKN) program. Doctor's prescription is one indicator of the quality of hospital services. Prescribing drugs based on guidelines will provide efficiency in the supply of drugs. The purpose of this study was to facilitate controlling drug supplies, safe use of drugs and control costs and quality of treatment. K-Means Clustering is a method of grouping data into clusters using the K-Means algorithm. The data used in this study was a specialist doctor's prescription in December 2019 which was sourced from the Pharmacy department of the Meranti Islands District Hospital. The results of this research with the K-Means Clustering method consisted of 3 (three) clusters, namely cluster 0 obeying Fornas as many as 2 polyclinics, cluster 1 being less obedient to Fornas as many as 2 polyclinics and cluster 2 not obeying Fornas as many as 3 polyclinics. This research can be used as a reference and evaluation to hospital management on the efficiency level of using specialist doctor's prescriptions in improving the quality of hospital services.

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Section: Pendahuluanunclassified

Tingkat Efisiensi Penggunaan Resep Dokter Spesialis Menggunakan Metode K-Means Clustering

sharon

Defit

Nurcahyo

2021

jidt

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“…Additionally, GMM can group complex patterns into similar components that match closely while k-means uses simple principles to produce only abstract information. The performance and comparison of the sampling methods used in GMM are reported in [ 26 ].…”

Section: Gaussian Mixture Model Clustering Technique and Classification Modelmentioning

confidence: 99%

Application of the Gaussian Mixture Model to Classify Stages of Electrical Tree Growth in Epoxy Resin

Mas’ud

Sundaram

Ardila-Rey

et al. 2021

Sensors

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In high-voltage (HV) insulation, electrical trees are an important degradation phenomenon strongly linked to partial discharge (PD) activity. Their initiation and development have attracted the attention of the research community and better understanding and characterization of the phenomenon are needed. They are very damaging and develop through the insulation material forming a discharge conduction path. Therefore, it is important to adequately measure and characterize tree growth before it can lead to complete failure of the system. In this paper, the Gaussian mixture model (GMM) has been applied to cluster and classify the different growth stages of electrical trees in epoxy resin insulation. First, tree growth experiments were conducted, and PD data captured from the initial to breakdown stage of the tree growth in epoxy resin insulation. Second, the GMM was applied to categorize the different electrical tree stages into clusters. The results show that PD dynamics vary with different stress voltages and tree growth stages. The electrical tree patterns with shorter breakdown times had identical clusters throughout the degradation stages. The breakdown time can be a key factor in determining the degradation levels of PD patterns emanating from trees in epoxy resin. This is important in order to determine the severity of electrical treeing degradation, and, therefore, to perform efficient asset management. The novelty of the work presented in this paper is that for the first time the GMM has been applied for electrical tree growth classification and the optimal values for the hyperparameters, i.e., the number of clusters and the appropriate covariance structure, have been determined for the different electrical tree clusters.

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Section: Clustering Analysismentioning

confidence: 99%

“…The K-means algorithm groups data points by using the distance from a cluster centroid [38] . It is widely used in scientific and industrial applications due to its simplicity and speed [38] . However, K-means uses Euclidean distance as the similarity measure which limits identification of nonlinear usage structures.…”

Section: Clustering Analysismentioning

confidence: 99%

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A Visual Analytics Framework from Geological Modeling to Reservoir Simulation

Cartwright

Lei

et al. 2021

Preprint

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Immersive technologies such as virtual reality has shown great potential in to enhance many important workflows the oil and gas industry. Immersive technologies have many natural advantages. These include enhanced spatial perception of 3D data, spatial user interfaces and interactions, the ability to provide collaboration space and incorporate analysis techniques. To take advantage of these technologies in the reservoir engineering domain, a visual analytics framework is established to demonstrate how these technologies may be used effectively. This framework allows a user to progress from geological modeling to reservoir simulation in an interactive and highly effective application. First, geological uncertainty analysis is conducted to screen representative realizations as candidates for simulation. Visualization and analysis of these representative realizations may then be performed in virtual reality using reservoir connectivity analysis of a tight oil reservoir. This application supports natural interactions, improved working space, and effective perception of underground connectivity. For this reason, it is more convenient and natural to work between a reservoir scale model and a set of candidate local realizations. This platform provides a basis for future data analysis methods and ways of interacting and visualizing data that support this analysis.

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Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model

Cited by 123 publications

References 13 publications

Tingkat Efisiensi Penggunaan Resep Dokter Spesialis Menggunakan Metode K-Means Clustering

Tingkat Efisiensi Penggunaan Resep Dokter Spesialis Menggunakan Metode K-Means Clustering

Application of the Gaussian Mixture Model to Classify Stages of Electrical Tree Growth in Epoxy Resin

A Visual Analytics Framework from Geological Modeling to Reservoir Simulation

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