Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index

Zhu, En; Yuan-xiang, Zhang; Wen, Peng; Feng, Liang

doi:10.1016/j.neucom.2019.07.048

Cited by 57 publications

(28 citation statements)

References 27 publications

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“…Researchers rely on experience and domain knowledge; according to the specific application, they determine the selection of parameters. Parameter setting is often a cumbersome process because sometimes we must experiment constantly to see what kind of parameter setting can get a better clustering effect, and without the participation of domain experts, only relying on the algorithm itself, generally, cannot get satisfactory results, so we need to have a certain understanding of the field of clustering samples [25][26][27].…”

Section: Steps Of Cluster Analysismentioning

confidence: 99%

[Retracted] Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm

Chu

Yin

2021

Complexity

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Mental health is an important basic condition for college students to become adults. Educators gradually attach importance to strengthening the mental health education of college students. This paper makes a detailed analysis and research on college students’ mental health, expounds the development and application of clustering analysis algorithm, applies the distance formula and clustering criterion function commonly used in clustering analysis, and makes a specific description of some classic algorithms of clustering analysis. Based on expounding the advantages and disadvantages of fast-clustering analysis algorithm and hierarchical clustering analysis algorithm, this paper introduces the concept of the two-step clustering algorithm, discusses the algorithm flow of clustering model in detail, and gives the algorithm flow chart. The main work of this paper is to analyze the clustering algorithm of students’ mental health database formed by mental health assessment tool test, establish a data mining model, mine the database, analyze the state characteristics of different college students’ mental health, and provide corresponding solutions. In order to meet the needs of the psychological management system based on the clustering analysis method, the clustering analysis algorithm is used to cluster the data. Based on the original database, this paper establishes the methods of selecting, cleaning, and transforming the data of students’ psychological archives. Finally, it expounds on the application of data mining in students’ psychological management system and summarizes and prospects the implementation of the system.

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Section: Steps Of Cluster Analysismentioning

confidence: 99%

[Retracted] Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm

Chu

Yin

2021

Complexity

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“…Grid-mapping, used in image recognition or pattern recognition, is another area where clustering is effective. Noise from uncertain sensor measurements can be reevaluated (Zhu et al 2019), have highlighted effective results.…”

Section: Clusteringmentioning

confidence: 99%

Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

Black

Richmond

Kolios

2021

International Journal of Sustainable Energy

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Information is key. Offshore wind farms are installed with supervisory control and data acquisition systems (SCADA) gathering valuable information. Determining the precise condition of an asset is essential on achieving the expected operational lifetime and efficiency. Equipment fault detection is necessary to achieve this. This paper presents a systematic literature review of machine learning methods applied to condition monitoring systems, using both vibration information and SCADA data together. Starting with conventional methods using vibration models, such as Fast-Fourier transforms to five prominent supervised learning regression models; Artificial neural network, support vector regression, Bayesian network, random forest and K-nearest neighbour. This review specifically looks at how conventional vibration data can be combined with SCADA data to determine the assets condition.

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“…The use of clustering algorithms is still very relevant for solving various problems. This could be seen from several recent studies that use clustering algorithms for various needs [11] [12]. The clustering method is a machine learning technique that could be used to group data, especially past data(related to data mining) that could be used for future solutions [13] [14].…”

Section: Figure 2 the Research Stepsmentioning

confidence: 99%

The Clustering of Paddy Fields Using Machine Learning Algorithms in the Province of South Sumatera

Suryati¹,

Sanmorino²,

Ermatita³

2020

Proceedings of the 3rd Forum in Research, Science, and Technology (FIRST 2019)

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In this paper, we were clustering paddy fields in the province of South Sumatera using machine learning algorithms. We chose machine learning algorithms because this algorithm is very relevant to the problem to be studied. The contribution of this study is in the form of result from the clustering of paddy fields which used as input or consideration for the government in determining policies related to the farming area. Policies that can use the results of the clustering of paddy fields include determining the most appropriate area for expanding paddy fields in the province of South Sumatera. The results of clustering using machine learning techniques show the expansion of paddy fields could be done in 13 districts or as much as 76.4% of the total districts in the province of South Sumatera.

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Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index

Cited by 57 publications

References 27 publications

[Retracted] Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm

[Retracted] Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm

Condition monitoring systems: a systematic literature review on machine-learning methods improving offshore-wind turbine operational management

The Clustering of Paddy Fields Using Machine Learning Algorithms in the Province of South Sumatera

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