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Research on prediction and recommendation of financial stocks based on k-means clustering algorithm optimization

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1081-1089页

作者： Fang, Zheng Chiao, Chaoshin Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Jiangsu Peoples R China Natl Dong Hwa Univ Dept Finance Hualien Taiwan

The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of k-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the k-means algorithm briefly and analyzed its advantages and disadvantages. Then, the k-means algorithm was optimized by introducing artificial fish swarm algorithm (AFSA) to obtain kAFSA. Then 100 stocks of listed companies were taken as the research subject and predicted by kAFSA designed in this study. The prediction results were verified through closing price, price earning ratio, earnings per share and return on net assets. The results showed that there were obvious differences between A and B stocks divided by kAFSA, and the differences of B stocks were significantly larger than those of A stocks. It shows that 100 stocks are well divided into high performance stocks and poor performance stocks through clustering, which provides a good reference for investors to invest in stocks and is worth of further application.

关键词： k-means clustering algorithm data mining artificial fish swarm algorithm Stock forecast

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Optimizing High School English Textbook Content Structure Using the k-means clustering algorithm

Optimizing High School English Textbook Content Structure Us...

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International Conference on Informatics Education and Computer Technology Applications (IECA)

作者： Du, Yutong Soochow Univ Sch Foreign Language Suzhou 215006 Peoples R China

ISBN: (纸本)9798350375343;9798350375336

Traditional high school English textbooks often need to be revised to address the varied learning preferences of students, resulting in disparities in teaching efficacy. This study takes the 2020 Yilin Edition of the high school English textbook as an example for analysis due to its widespread use and adherence to contemporary educational standards. It aims to remedy these gaps by applying a cluster-based analytical method to reform textbook content, focusing on individual differences. Through a detailed analysis of language difficulty, content breadth, and stylistic differences, the cluster-based analytical method reveals hidden patterns in textbook content distribution that provide empirical support for adjusting textbook structure and allocating personalized content. The findings indicate that this method effectively identifies textbook imbalances while offering a scientific direction for optimizing course design. The research underscores the utility of data mining in education and propounds an advisable methodology for advancing high school English instruction.

关键词： High school English teaching k-means clustering algorithm Textbook optimization Data analysis

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A Prediction Model of Hard landing Based on RBF Neural Network with k-means clustering algorithm

A Prediction Model of Hard landing Based on RBF Neural Netwo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Qiao, Xiaoduo Chang, Wenbing Zhou, Shenghan Lu, Xuefeng Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China

ISBN: (纸本)9781509036653

This paper proposes a prediction model for forecasting the hard landing problem. The landing phase has been demonstrated the most dangerous phase in flight cycle for fatal accidents. The landing safety problem has become one of the hot research problems in engineering management field. The study concentrates more on the prediction and advanced warning of hard landing. Firstly, flight data is preprocessed with data slicing method based on flight height and dimension reduction. Subsequently, the radial basis function (RBF) neural network model is established to predict the hard landing. Then, the structure parameters of the model are determined by the k-means clustering algorithm. In the end, compared with Support Vector Machine and BP neural network, the RBF neural network based on k-means clustering algorithm model is adopted and the prediction accuracy of hard landing is better than traditional ways.

关键词： flight data hard landing prediction model RBF neural network k-means clustering algorithm

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Airport Role Orientation Based on Improved k-means clustering algorithm 3rd

Airport Role Orientation Based on Improved K-means Clusterin...

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3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Xia, Qingjun Zhang, Zhaoyue Zhang, Baochen Civil Aviat Univ China Coll Air Traff Management Tianjin Peoples R China

ISBN: (纸本)9783030364052;9783030364045

This paper aims to provide an insight into the roles of the different types of airports in China by improved k-means clustering algorithm. The first part of the work analyzed the characteristics of Chinese airline network and pointed out that the key to construct hub-and-spoke airline network is determining the function of each airport. The index system of airport function orientation was established from airport operation index, airport hinterland index and airport growth index. The airports in China were classified into four classes by the k-means clustering algorithm. In order to improve reliability of clustering algorithm, a formula was used to normalize the value of each index, and the airports were clustered by improved k-means clustering algorithm. The algorithm was simulated by the MATLAB and the clustered results show the airports have obvious hierarchy.

关键词： k-means clustering algorithm Hub airport Trunk airport Line airport

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THE ESTIMATION OF THE LAMB WAVE PHASE VELOCITY DISPERSION CURVES BASE ON k- means clustering algorithm 15

THE ESTIMATION OF THE LAMB WAVE PHASE VELOCITY DISPERSION CU...

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15th Symposium on Piezoelectrcity, Acoustic Waves and Device Applications (SPAWDA)

作者： Gao, Xiang Li, Chen-xu Jiao, Jing-pin Feng, Ze-wen Beijing Univ Technol Fac Mat & Mfg Beijing 100049 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China

ISBN: (纸本)9781665403153

Lamb waves are widely used in the localization and quantification of structural damage for plate-type structure. The propagation velocity and dispersion characteristics of Lamb waves are very important calculation parameters. The method to estimate the dispersion curves of Lamb waves is proposed based on k-means clustering algorithm. Three piezoelectric transducers with different center frequencies experimentally transmit the Lamb wave signals, and laser vibrometer is used to receive signals at different intervals in the direction of wave propagation. The phase velocity of lamb waves is estimated by many pairs of adjacent signals peaks. Use Euclidean distance as the index of data evaluation to find the data center, which is the phase velocity to be solved. And the dispersion curves are further estimated and inverted. The proposed method has been experimentally verified in isotropic-aluminum plate. The relative error of the actual phase velocity of lamb waves compared with all measured clustering centers is less than 1%, and the overall average relative error is less than 0.4%.

关键词： Lamb waves Phase velocity Dispersion curves k-means clustering algorithm

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Research on Improved k-means clustering algorithm

Research on Improved K-means Clustering Algorithm

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Zhang, Yinsheng Shan, Huilin Li, Jiaqiang Zhou, Jie Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9783037853122

The traditional k-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster center. Hierarchical clustering algorithm can be used to generate the initial cluster center of k-means clustering algorithm. The geometric features of input data can achieve a good distribution by means of pretreatment and feature extraction and selection. In the learning of fuzzy neural network, Java language is used to write source code of the algorithm. The experimental results show that new algorithm has improved the clustering quality effectively.

关键词： Pattern Recognition Hierarchical clustering k-means clustering algorithm Java

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k-means clustering algorithm for data distribution in cloud computing environment

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2021年第3期12卷 322-331页

作者： Pan, Hailan Lei, Yongmei Yin, Shi Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Polytech Univ Res Ctr Resource Recycling Sci & Engn Shanghai 201209 Peoples R China Shanghai Polytech Univ Sch Econ & Management Shanghai 201029 Peoples R China

This study analyses the data structure in cluster analysis. It is a clustering method that randomly selects a known number of points and then continues to expand. Through the comparative experiments on the clustering accuracy of different similarity matrices, the experimental analysis on the effectiveness of the model, the distribution of e-commerce data under cloud computing and the calculation time of different clustering algorithms, we can better understand the k-means clustering algorithm and the status of e-commerce in cloud computing environment. The experimental results show that if the appropriate similarity function is selected, the result of spectral clustering is usually not lower than that of simple k-means clustering. When the number of users reaches 4000, the list reading time of the k-means clustering algorithm is 3.15 s, while the other three algorithms consume more time.

关键词： cloud computing data distribution oriented k-means clustering algorithm e-commerce platform data structure data distribution

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Research of outliers in time series of stock prices based on improved k-means clustering algorithm

Research of outliers in time series of stock prices based on...

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International Conference of Information Science and Management Engineering (ISME)

作者： Zhu, Xi Che, Wen-Gang Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming Peoples R China

ISBN: (纸本)9781845648299;9781845648282

The outlier in financial time series usually can affect the trend of the series, at the same time, it often indicates social public information to the influence of the stock market. So how to detect the outlier in financial time series effectively is of great significance. This paper is based on the clustering of the outlier detection by the idea, and put forward an improved k-means clustering algorithm. The main feature of the improved algorithm is to use the idea of the clustering of the outlier detection, this algorithm used the inherent disadvantage of the k-means algorithm which is easy to fall into the local extrema. So we changed clusters k and made clusters k=10k. Then we let k decrease to k in a certain step size, In this process, we had repeated the cycle to identify the outlier, so that we could ensure this algorithm will not always fall into the local extrema. In this paper, on the closing price of the Shanghai Composite Index and China International Trade Stock and macroeconomic information empirical analysis, we prove that the method is feasible and effective.

关键词： Time series outlier k-means clustering algorithm local extrema

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Inferring Station Numbers in Metro Trips Using Mobile Magnetometer Sensor via an Unsupervised k-means clustering algorithm 8

Inferring Station Numbers in Metro Trips Using Mobile Magnet...

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8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Hosseini, Seyed Hassan Gentile, Guido Varghese, ken koshy Miristice, Lory Michelle Bresciani Univ Roma La Sapienza Dept Civil Construct & Environm Engn Rome Italy

ISBN: (纸本)9781665455305

Transport mode detection in urban areas with the help of mobile phones is not anymore, a challenging problem. Most studies via machine and deep learning models using GPS and inertial mobile sensors can distinguish different modes with various prediction accuracies. This paper presents a new way to count the number of stations in metro trips using a magnetometer sensor where GPS, internet, and wireless positioning are unavailable. The primary source for this investigation was recorded via mobile magnetometer sensor of metro riders. We first find contextual features that can effectively recognize acceleration state according to the 3D magnetometer data and then classification with a k-means unsupervised method into different classes. Finally, we present a station counter algorithm to count the number of metro stations in metro-based trips. Results from experiments in Rome and Stockholm metro systems show that our final algorithm can count the number of stations with an accuracy of 86 % where there is no internet, GPS, and WiFi access.

关键词： metro station detection magnetometer sensor mobile data k-means clustering algorithm

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An Evaluation Strategy of Energy Storage Construction for Industrial Users Based on k-means clustering algorithm

An Evaluation Strategy of Energy Storage Construction for In...

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IEEE International Conference on Computing, Communications and IoT Applications (IEEE ComComAp)

作者： Zhao, Yangyang Deng, Boya Chen, Hui Zhang, Hualu Shi, Jie Xu, Zhengwei Zhu, Xingyang Han, Guangjie State Grid Suzhou City Energy Res Inst Co Ltd Suzhou Peoples R China State Grid Suzhou Power Supply Co Ltd Suzhou Peoples R China NARI Informat & Commun Technol Co Ltd Nanjing Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781728119731

Energy storage refers to a series of related technologies that achieve energy reserve and release by some ways when needed. Energy storage technology plays an important role in peak shaving and valley filling, improving the equipment utilization efficiency, enhancing the system safety and so on. It has great significances to realize the transformation of energy structure and the change of electricity production or consumption patterns for promoting the overall development of energy industry through the research of user-side energy storage technology. In this paper, we analyze the historical power consumption data of industrial users by k-means clustering algorithm to design a user type automatic evaluation model, which can assist large industrial users to judge whether industrial users are suitable for energy storage construction or not, it can help enterprises save manpower and financial resources. In addition, we design a method to calculate the potential value of users, which can reflect the degree of suitability for equipping energy storage device. Simulation results show that the proposed model can correctly obtain the users' typical load curve and power usage type through clustering historical power load data.

关键词： k-means clustering algorithm Energy storage Peak shaving and valley filling

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