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Open cluster membership probability based on k-means clustering algorithm

EXPERIMENTAL ASTRONOMY 2016年第1期42卷 49-59页

作者： Abd El Aziz, Mohamed Selim, I. M. Essam, A. Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Nahda Univ Fac Comp Sci Bani Suwayf Egypt Natl Res Inst Astron & Geophys Dept Astron Cairo Egypt HTI Cairo Egypt

In the field of galaxies images, the relative coordinate positions of each star with respect to all the other stars are adapted. Therefore the membership of star cluster will be adapted by two basic criterions, one for geometric membership and other for physical (photometric) membership. So in this paper, we presented a new method for the determination of open cluster membership based on k-means clustering algorithm. This algorithm allows us to efficiently discriminate the cluster membership from the field stars. To validate the method we applied it on NGC 188 and NGC 2266, membership stars in these clusters have been obtained. The color-magnitude diagram of the membership stars is significantly clearer and shows a well-defined main sequence and a red giant branch in NGC 188, which allows us to better constrain the cluster members and estimate their physical parameters. The membership probabilities have been calculated and compared to those obtained by the other methods. The results show that the k-means clustering algorithm can effectively select probable member stars in space without any assumption about the spatial distribution of stars in cluster or field. The similarity of our results is in a good agreement with results derived by previous works.

关键词： Galaxy Open clusters and associations Individual k-means clustering algorithm NGC 2266 NGC 188 Color-magnitude diagram (CMD)

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Improving performance of classification on severity of ill effects (SEV) index on fish using k-means clustering algorithm with various distance metrics

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WATER PRACTICE AND TECHNOLOGY 2019年第1期14卷 101-117页

作者： Hamid, khakzad RusPers Grp Co Dept Engn Moscow 109263 Russia

The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports and consultations, and described as a function of duration of exposure to turbid conditions in fisheries or fish life stages by fish adapted to life in clear water ecosystems. In this study, the performance of classification by SEV index was investigated using the k-means clustering algorithm. This study is based on 303 tests undertaken on aquatic ecosystem quality over a wide range of sediment concentrations (1-50,000 mg SS/L) and durations of exposure (1-35,000 h). Training and testing data includes concentration of suspended sediment, duration of exposure, species and life stages as the input variables and the SEV index for fish as the output variable. Results indicate that the k-means clustering algorithm, as an efficient novel approach with an acceptable range of error, can be used successfully for improving the performance of classification by SEV index.

关键词： k-means clustering algorithm scale of the severity (SEV) sediment management turbidity currents

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Dk-means: a deterministic k-means clustering algorithm for gene expression analysis

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PATTERN ANALYSIS AND APPLICATIONS 2019年第2期22卷 649-667页

作者： Jothi, R. Mohanty, Sraban kumar Ojha, Aparajita Pandit Deendayal Petr Univ Sch Technol Dept Comp Engn Gandhinagar India Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur Madhya Pradesh India

clustering has been widely applied in interpreting the underlying patterns in microarray gene expression profiles, and many clustering algorithms have been devised for the same. k-means is one of the popular algorithms for gene data clustering due to its simplicity and computational efficiency. But, k-means algorithm is highly sensitive to the choice of initial cluster centers. Thus, the algorithm easily gets trapped with local optimum if the initial centers are chosen randomly. This paper proposes a deterministic initialization algorithm for k-means (Dk-means) by exploring a set of probable centers through a constrained bi-partitioning approach. The proposed algorithm is compared with classical k-means with random initialization and improved k-means variants such as k-means++ and MinMax algorithms. It is also compared with three deterministic initialization methods. Experimental analysis on gene expression datasets demonstrates that Dk-means achieves improved results in terms of faster and stable convergence, and better cluster quality as compared to other algorithms.

关键词： k-means clustering algorithm Initial cluster centers Gene expression clustering Microarray data analysis

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

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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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Research on parallel association rule mining of big data based on an improved k-means clustering algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第3期16卷 233-247页

作者： Hao, Li Wang, Tuanbu Guo, Chaoping Xijing Univ Coll Informat Engn Xian 710123 Peoples R China

In order to overcome the problems of time-consuming, low-precision and redundant rules in association rule mining of big data, a parallel association rule mining method based on an improved k-means clustering algorithm is proposed. Establish a data object criterion function and optimise k-means clustering algorithm. The improved k-means clustering algorithm is used to cluster big data and improve the efficiency of mining association rules. This paper introduces the matter-element theory of extension, combines matter-element theory and database, and constructs the matter-element relation database model of extension to realise the mining of parallel association rules in big data on the basis of extension. Redundant algorithms and equivalent transformations are used to eliminate redundant association rules. The experimental results show that the proposed method has high mining efficiency, high mining accuracy, and high rule association, which proves that the proposed method has better application performance.

关键词： k-means clustering algorithm association rules data mining redundancy algorithm equivalence transformation

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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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Optimal Cell Screening Method for Minimizing the Imbalance Based on k-means clustering algorithm 21

Optimal Cell Screening Method for Minimizing the Imbalance B...

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21st International Conference on Electrical Machines and Systems (ICEMS)

作者： Lee, Pyeong-Yeon Han, Dong-Ho kim, Gun-Woo kang, Tae-Woo Pack, Sung-Baek kim, Jonghoon Chungnam Natl Univ Elect Engn Daejeon South Korea Korea Inst Nucl Safety Nucl Safety Res Team Daejeon South Korea

ISBN: (纸本)9788986510201

An imbalance of the battery pack in the voltage and state of charge(SOC) leads to problems in the lifetime, performance, and safety. To efficiently operate the battery pack in the power-driven systems, each cell in the battery pack has similar electrochemical characteristics. This paper proposes the optimal cell screening method using k-means clustering algorithm, which is one of the unsupervised learning methods. To investigate the proposed scheme, 2000 units of 18650 Li-ion cell are used in this paper. The results of the verification demonstrate that the proposed screening method minimizes the cell imbalance.

关键词： k-means clustering algorithm li-ion battery internal resistance cell screening

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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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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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