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Analysis and Prediction of College Students' Mental Health Based on k-means Clustering algorithm

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期7卷 501-512页

作者： Liu, Yanwu Beijing Polytech Beijing 100176 Peoples R China

Mental health is an important basic condition for the adult development of college students, and education workers gradually pay attention to the strengthening of mental health education for college students. In this paper, a psychological management system based on the k-means clustering analysis method is proposed. Based on the basic functions of the traditional system, the students' psychological data are reutilised by using the idea of data mining. By optimising the iterative process of the k-means algorithm, the valuable parts of a large number of precipitation students' psychological data are extracted. A data model is established and it provides decision guidance for managers to scientifically manage students' mental health process. The system establishes the data mining model in the process of analysis, carries on the mining to the students' psychological data in the database, analyses the different college students' mental health state characteristics and provides the corresponding solution. The part of the test drew on mental health data from 1,000 students at a school, and the results show that the system uses the k-means algorithm to divide students into 3 categories, which are 20.6%, 31.9% and 47.1% respectively, which are 1.6% different from the data test results in the ideal state.

关键词： Data mining psychological management system k-means algorithm

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Research on improved RFM customer segmentation model based on k-means algorithm 5

Research on improved RFM customer segmentation model based o...

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5th International Conference on Computational Intelligence and Applications (ICCIA)

作者： Huang, Yong Zhang, Mingzhen He, Yue Sichuan Univ Business Sch Chengdu Peoples R China

ISBN: (纸本)9781728160429

The RFM model used for customer segmentation in the traditional retail industry is not suitable for the industry with distinct attributes of social groups, so the RFMC model is created by introducing the parameter C of social relations. Educational e-commerce enterprise M is selected for empirical study, and k-means algorithm is used for cluster analysis of valid customers of enterprise M, which resulted in 5 distinct customer groups and verified the effectiveness of the model.

关键词： k-means algorithm Improved RFM model customer segmentation Educational e-commerce

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Evaluation of Class Decomposition based on Clustering Validity and k-means algorithm 21

Evaluation of Class Decomposition based on Clustering Validi...

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21st International Arab Conference on Information Technology (ACIT)

作者： Sowan, Bilal Matar, Nasim Omar, Firas Alauthman, Mohammad Eshtay, Mohammed Univ Petra Dept Business Intelligence & Data Analyt Amman Jordan Univ Petra Dept E Business & Commerce Amman Jordan Univ Petra Dept Informat Secur Amman Jordan LTUC Dept ASAC Amman Jordan

ISBN: (纸本)9781728188553

A class decomposition is one of the possible solutions and the most important factors of success for the improvement of classification performance. The idea is to transform a dataset by categorizing each class label into groups or clusters. Thus, the transformation is done concerning data characteristics and similarities. This paper proposed a hybrid model for a class decomposition by the integration of gap statistic, k-means clustering algorithm, and Naive Bayes classifier. The model is based on clustering validity using gap statistic for enhancing the classifier performance. The model works by dividing each dataset into several subsets regarding its class labels. After that, the clustering validity using gap statistic is employed for estimating the optimal number of clusters for each subset that belong to a particular class label. The estimated number of clusters is used then as an input parameter for the k-means clustering algorithm for relabeling the data objects with a new class label in each subset. Every data object is allocated to each of the clusters generated by the k-means clustering algorithm, which consider it as the new class label. The proposed model integrates the class decomposition approach with Naive Bayes classifier to compare the performance of the proposed model under several classification measures. The model is validated and evaluated by employing different real-world datasets collected from the UCI machine learning repository. The experimental results show that a significant improvement in classification accuracy and F-measure when the class decomposition is applied. Also, the experiments indicate that using a class decomposition is not appropriate for all datasets.

关键词： Class Decomposition Clustering Validity k-means algorithm Classification Naive Bayes

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GBk-means clustering algorithm: An improvement to the k-means algorithm based on the bargaining game

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kNOWLEDGE-BASED SYSTEMS 2021年 213卷 106672-106672页

作者： Rezaee, Mustafa Jahangoshai Eshkevari, Milad Saberi, Morteza Hussain, Omar Urmia Univ Technol Fac Ind Engn Orumiyeh Iran Univ Technol Sydney Sch Informat Syst & Modelling Sydney NSW Australia Univ New South Wales Sch Business Canberra ACT Australia

Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-means is one of the well-known algorithms for clustering data. The foundation of this algorithm is based on the distance measure. However, the traditional k-means has some weaknesses that appear in some data sets related to real applications, the most important of which is to consider only the distance criterion for clustering. Various studies have been conducted to address each of these weaknesses to achieve a balance between quality and efficiency. In this paper, a novel k-means variant of the original algorithm is proposed. This approach leverages the power of bargaining game modelling in the k-means algorithm for clustering data. In this novel setting, cluster centres compete with each other to attract the largest number of similar objectives or entities to their cluster. Thus, the centres keep changing their positions so that they have smaller distances with the maximum possible data than other cluster centres. We name this new algorithm the game-based k-means (GBk-means) algorithm. To show the superiority and efficiency of GBk-means over conventional clustering algorithms, namely, k-means and fuzzy k-means, we use the following syntactic and real-world data sets: (1) a series of two-dimensional syntactic data sets;and (2) ten benchmark data sets that are widely used in different clustering studies. The evaluation criteria show GBk-means is able to cluster data more accurately than classical algorithms based on eight evaluation metrics, namely F-measure, the Dunn index (DI), the rand index (RI), the Jaccard index (JI), normalized mutual information (NMI), normalized variation of information (NVI), the measure of concordance and error rate (ER). (C) 2020 Elsevier B.V. All rights reserved.

关键词： k-means algorithm Bargaining game Cluster centre competition Clustering improvement Maximum data coverage

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Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-k-means algorithm

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AD HOC NETWORkS 2021年 122卷 102660-102660页

作者： Chowdhury, Aparajita De, Debashis Techno Main Salt Lake Dept Informat Technol Kolkata 700091 W Bengal India Maulana Abul Kalam Azad Univ Technol Ctr Mobile Cloud Comp Dept Comp Sci & Engn Kolkata 700064 W Bengal India Univ Western Australia Perth WA 6009 Australia

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-k-means algorithm. In this approach, Glowworm Swarm Optimization, k-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Further-more, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.

关键词： Coverage maximization Lifetime enhancement Glowworm swarm optimization Voronoi diagram k-means algorithm

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Research on fault diagnosis method of control moment gyroscope based on k-means algorithm 39

Research on fault diagnosis method of control moment gyrosco...

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39th Chinese Control Conference (CCC)

作者： Liu, Wenjing Yuan, Li Wang, Shuyi Liu, Chengrui Beijing Inst Control Engn Sci & Technol Space Intelligent Control Lab Beijing 100094 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

A fault diagnosis method of control moment gyroscope (CMG) based on k-means algorithm is proposed to improve the fault diagnosis accuracy by considering the hidden correlation between multidimensional telemetry data. Principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are used to extract features of CMG digital and physical data. On this basis, the fault diagnosis process of CMG based on k-means algorithm is supplied. Finally, the effectiveness of the proposed method is verified by using the actual in-orbit CMG data.

关键词： Control moment gyroscope Fault diagnosis PCA t-SNE k-means algorithm

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A kernel Iterative k-means algorithm 40th

A Kernel Iterative K-Means Algorithm

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40th Anniversary International Conference on Information Systems Architecture and Technology (ISAT)

作者： Falkowski, Bernd-Juergen Fachhsch Okon & Management FOM Arnulfstr 30 D-80335 Munich Germany

ISBN: (纸本)9783030306045;9783030306038

In this paper Mercer kernels with certain invariance properties are briefly introduced and an apparently not well-known construction using certain cohomology groups is described. As a consequence some kernels arising from this are given. Hence a kernel version of an iterative k-means algorithm due to Duda et al. is exhibited. It resembles the usual k-means algorithm but relies on a different update procedure and allows an elegant computation of the target function.

关键词： k-means algorithm Mercer kernels Cohomology groups

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Smart Health Predicting System Using k-means algorithm 10

Smart Health Predicting System Using K-Means Algorithm

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10th International Conference on Computer Communication and Informatics (ICCCI)

作者： Ravuvar, Harshitha Naidu Goda, Haritha Sumathi, R. Chinnasamy, P. Kalasalingam Acad Res & Educ Dept Comp Sci & Engn CSE Krishnankoil Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil Tamil Nadu India Sri Shakthi Inst Engn & Technol Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (数字)9781728145143

ISBN: (纸本)9781728145136

This main goal of this Appis mainly focus on the predicting the disease based on the given symptoms by the user through registration. The patient can able to get the doctor details and address of the hospital immediately by giving the disease and particular location where he requires. He gets the appointment from various doctors for respective treatments. The user can able to know the information regarding the acceptance and rejection of their appointment. If the appointment is accepted simultaneously, the bills can be sent to the respective user and he can pay the amount through this app.

关键词： Health Prediction Symptoms k-means algorithm Doctor Appointment Location

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The Border k-means Clustering algorithm for One Dimensional Data

The Border K-Means Clustering Algorithm for One Dimensional ...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Froese, Ryan klassen, James W. Leung, Carson k. Loewen, Tyler S. Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781665421973

Clustering has been widely used for data preprocessing, mining, and analysis. The k-means clustering algorithm is commonly used because of its simplicity and flexibility to work in many real-life applications and services. Despite being commonly used, the k-means algorithm suffers from non-deterministic results and run times that greatly vary depending on the initial selection of cluster centroids. To improve the k-means algorithm, we present in this paper a border k-means clustering algorithm. It combines concepts from the k-means algorithm with an additional focus on the concepts of the borders dividing clusters. Consequently, the resulting border k-means algorithm leads to deterministic results and a great reduction in run time when compared with the traditional k-means algorithm.

关键词： Big data smart computing data mining machine learning clustering cluster analysis k-means algorithm data analysis

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A k-means algorithm Based Platform for the Assistant Decision-Making of Power Grid Dispatching

A K-means Algorithm Based Platform for the Assistant Decisio...

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2021年第四届算法、计算和人工智能国际会议

作者： Zhenfan Yu Xinlei Cai Jinzhou Zhu Xu Lin Power Dispatching and Controlling Center Guangdong Power Grid Company Limited

One limitation of the traditional assistant decision-making platform for power grid dispatching is that the data clustering is time consuming, which decreases the efficiency of power grid dispatching and threatens the safety of power grid. In this study, a k-means algorithm based platform for the assistant decision-making of power grid dispatching is designed and established, by integrating data from the analysis of equipment operations, the intelligent monitoring and early warning of the power grid and the analysis of power grid operations. In the designed platform, the feature library of a power grid is obtained by establishing a multi-layer decision tree model, the feature library is classified by k-means algorithm, and the classified information of power grid is then filtered to obtain reliable data sections to support the assistant decision-making of power grid dispatching. Finally, the performance of the proposed platform is verified by experimental results, which demonstrates that the designed platform in this paper has shorter clustering time than the traditional assistant decision-making platform for power grid dispatching, thus ensuring the safe and stable operation of power grid.

关键词： k-means algorithm power grid dispatch auxiliary decisionmaking operation section

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