Improvement of K-Means Algorithm for Accelerated Big Data Clustering

Wu, Chunqiong; Yan, Bingwen; Yu, Rongrui; Zhang-shu, Huang; Yu, Baoqin; Yu, Yanliang; Chen, Na; Zhou, Xiukao

doi:10.4018/ijitsa.2021070107

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Therefore, the difficulty of predicting a sample as a negative sample is much smaller than the difficulty of predicting it as a positive sample. As a result, this paper adopts the focal loss method for dense object detection (Lin et al, 2017;Wu et al, 2021)…”

Section: Prediction Modelmentioning

confidence: 99%

“…It can have better prediction results when the number of positive and negative samples in the training differs significantly. As described in the article on focal loss for dense object detection (Lin et al, 2017;Wu et al, 2021), adjusting the α parameter allows the model to focus more on positive samples with smaller sample sizes. Adjusting the γ parameter allows the model to focus more on more difficult-to-judge samples.…”

Section: Modeling and Tuningmentioning

confidence: 99%

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An Optimised Bitcoin Mining Strategy

Luo

Zhang

2023

International Journal of Information Technologies and Systems Approach

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Stale blocks are not avoidable in blockchain, such as the Bitcoin network, when proof-of-work is used as the consensus protocol. However, as the economic loss to the miners and the security risk to the network cannot be ignored, research is needed to identify and analyse stale blocks. By analysing the factors influencing the generation of stale blocks, the authors propose a new machine learning model based on XGBoost. They propose a new data collection method for bitcoin nodes to obtain real data for training prediction model. Then, based on the model, they generate optimal mining strategies and analyse the economic benefits. The experimental data and application cases show that the real-time data detection and machine learning model that they propose can accurately identify and predict the generation of stale blocks and generate an economically optimal mining strategy in the Bitcoin network with the presence of stale blocks.

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Section: Prediction Modelmentioning

confidence: 99%

Section: Modeling and Tuningmentioning

confidence: 99%

An Optimised Bitcoin Mining Strategy

Luo

Zhang

2023

International Journal of Information Technologies and Systems Approach

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show abstract

“…Traditional data analysis methods can effectively extract the features of data of low dimension. When the data dimension is too high, the effect of these methods will be significantly reduced (Wu, et al, 2021).…”

Section: Fault Identification Model Based On Improved Dbn Network Modelmentioning

confidence: 99%

Fault Analysis Method of Active Distribution Network Under Cloud Edge Architecture

Dong

Sha

Song

et al. 2023

International Journal of Information Technologies and Systems Approach

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Efficient fault treatment of active distribution network is an important guarantee to ensure the steady-state reliability of the system. In order to improve the accuracy of distribution network fault identification and analysis, a fault processing method based on deep learning is proposed in this paper. This method collects massive heterogeneous data sets using patrol robot to realize real-time perception and accurate acquisition of distribution network status. Relying on the processing mode of distribution network cloud edge collaboration, the principal component analysis method is used at the edge to effectively remove redundant data, providing a complete and reliable data support for the deep network model. Meanwhile, the attention mechanism is added to the cloud to improve the depth confidence network, further realizing the extraction of useful feature information for complex data sets and avoiding the interference of irrelevant information on the recognition results. The simulation experiment is based on the actual active distribution network model. The experimental results show that the fault identification accuracy of the proposed method can reach 0.9255, indicating an excellent distribution network fault identification and analysis ability to support safe operation of active distribution network.

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“…e COP metric measures the intracluster tightness of a class cluster in terms of the average distance from data objects within the class cluster to the class cluster centroid, and the intercluster separation of a class cluster in terms of the minimum of the maximum distance from data objects e COP index is a minimum value index, that is, the clustering algorithm has the best division effect when the index achieves the minimum value [21].…”

Section: K-means Algorithmmentioning

confidence: 99%

Professional Talent Training System for Landscape Engineering Based on K-Means Algorithm

Zhou¹

2022

Mobile Information Systems

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As the economy and society are developing and changing continuously, the garden industry is also developing and changing. The landscape industry has become a major focus of research on how to bring the training methods of the landscape technology profession more in line with the changing times, and how to make the training methods more in line with the needs of the industry by using modern personnel training methods. And it is important to find a good k-means algorithm to match the development of landscape engineering professionals. In this experiment, a combination of telephone interviews and questionnaires was used to ask questions about the landscape engineering profession. The respondents were the principals of the enterprise or relevant technical personnel. They learned what abilities the landscape engineering professionals needed by the society should have, and then the components were appropriate. The teaching system is used to conduct experimental teaching for landscape engineering majors, and there is also a landscape engineering professional control group for comparative analysis. The experimental results show that 27.45% of the students in the experimental group have 60–70 credits, while only 10.34% of the students in the control group have credits in this interval. The gap between the students in the two classes is very large, mainly because the experimental group pays attention to the combination of practice; practice and theory can better promote students’ mastery and application of professional knowledge. Moreover, 66.67% of the students in this experimental group found jobs in their majors. It can be seen from this that this system of cultivating talents for landscape engineering is very useful.

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Improvement of K-Means Algorithm for Accelerated Big Data Clustering

Cited by 7 publications

References 24 publications

An Optimised Bitcoin Mining Strategy

An Optimised Bitcoin Mining Strategy

Fault Analysis Method of Active Distribution Network Under Cloud Edge Architecture

Professional Talent Training System for Landscape Engineering Based on K-Means Algorithm

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