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16th International Conference on Machine Learning and Computing (ICMLC)

作者： Wang, Yakun Xu, Lili Mo, Liangze Nanning Univ Coll Digital Econ Nanning Peoples R China

ISBN: (纸本)9798400709234

In today's financial markets, Exchange-Traded Funds (ETFs), particularly high-liquidity equity ETFs exhibiting robust market liquidity, have garnered increasing attention from investors. This study conducts an analysis of 50 highly liquid equity Exchange-Traded Funds (ETFs) within the Chinese securities market, covering the period from October 25, 2022, to October 26, 2023. Employing the Sparse Inverse Covariance Estimation method (GraphicalLassoCV), the research calculates the conditional correlation among ETFs. Additionally, an unsupervised learning method, the affinity propagation algorithm from machine learning, is utilized for further analysis and exploration of the market structure of Chinese high-liquidity equity ETFs. Subsequently, 'manifold' techniques are applied for data visualization. The results reveal that 50 high-liquidity equity ETFs are divided into 11 clusters, and specific clusters exhibit industry cohesion, reflecting similar intraday volatility patterns within sectors. The Broad Market Indices and Banking Cluster and the Semiconductor and Artificial Intelligence Cluster are the largest and most active categories. They account for 31.88% and 24.87% of the total market capitalization, and 8.62% and 38.66% of the total trading volume, *** represents the trend over the past year, where short-term investors have shown a preference for innovative companies in sectors such as semiconductors and artificial intelligence. On the other hand, long-term capital tends to favor ETFs associated with banking, market indices, and other sectors characterized by high dividend yields and strong risk diversification capabilities.

关键词： affinity propagation algorithm Market structure Visualization research High-liquidity equity ETFs

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Adaptive affinity propagation algorithm Based on New Strategy of Dynamic Damping Factor and Preference

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第1期14卷 97-104页

作者： Hu, Jiusong Liu, Hongli Yan, Zhi Hunan Univ Coll Elect & Informat Engn Changsha Hunan Peoples R China

The affinity propagation algorithm has been extensively applied in various fields. However, it is still faces two severe challenges in actual applications: one is that the algorithm may be nonconvergent;and the other is that the convergence speed is low. Aiming at solving these two problems, an adaptive affinity propagation algorithm based on a new strategy of dynamic damping factor and preference is proposed in this paper. On one hand, the dynamic damping factor changes the factor value according to the check state of oscillation to eliminate and escape from the oscillation. On the other hand, dynamic preference adjusts the value of the preference based on the bisection and memory tuple to reduce the search scope of the target preferences continuously. Simulation results show that the proposed algorithms can solve the potential nonconvergence problem effectively and reduce the time consumed significantly. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： affinity propagation algorithm adaptive affinity propagation algorithm damping factor preference

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Identification of rock discontinuity sets based on a modified affinity propagation algorithm

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2017年 94卷 32-42页

作者： Liu, Jie Zhao, Xing-Dong Xu, Zeng-he Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

Identification of rock discontinuity sets is an important foundation for stability analysis of rock engineering applications. A modified affinity propagation (AP) algorithm is proposed for identifying rock discontinuity sets based on discontinuity orientations. The method considers all data points as potential clustering centers simultaneously, which could avoid the hard selection on initial clustering centers as well as achieve the global optimization. Euclidean distance measure in the original algorithm is not suitable for the clustering of discontinuity orientations and therefore a similarity measure based on the negative sine-squared value of the acute angle between discontinuity unit normal vectors was used. Moreover, the Silhouette validity index was introduced to determine the optimal clustering number. The validity of the new method was tested by using artificial data, and in-situ data compiled from the literature. Finally, the proposed method was applied to discontinuity grouping in an underground water-sealed oil storage cavern in Liaoning Province, China. The results show that the new method can effectively filter noisy data and achieve good clustering results with stronger robustness than other methods.

关键词： Rock discontinuity sets Cluster analysis affinity propagation algorithm Orientation analysis

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Gravity Theory-Based affinity propagation Clustering algorithm and Its Applications

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2018年第4期25卷 1125-1135页

作者： Wang, Limin Hao, Zhiyuan Han, Xuming Zhou, Ruihong Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Jilin Big Data Res Ctr Business Changchun 130117 Jilin Peoples R China Changchun Univ Technol Sch Comp Sci & Engn Changchun 130117 Jilin Peoples R China

The original affinity propagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data with high dimension and sparsity when the original algorithm was running. Due to the single calculation method of similarity, the convergence and clustering accuracy of the algorithm were greatly affected. On the other hand, in the universe, we can consider the formation of galaxies is a clustering process. In addition, the interaction between different celestial bodies are achieved through universal gravitation. This paper introduced the Density Peak clustering algorithm (DP) and gravitational thought into the AP algorithm, and constructed the density property to calculate the similarity, put forward the affinity propagation clustering algorithm based on Gravity (GAP). The proposed algorithm was more accurate to calculate similarity of simple points through the local density of corresponding points, and then used the gravity formula to update the similarity matrix. The data clustering process could be seen as the sample points spontaneously attract each other based on 'gravitation'. Experimental results showed that the convergence performance of GAP algorithm is obviously improved over the AP algorithm, and the clustering effect was better.

关键词： affinity propagation algorithm gravitation theory local density similarity matrix

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Semi-supervised affinity propagation Clustering algorithm Based On Kernel Function 27

Semi-supervised Affinity Propagation Clustering Algorithm Ba...

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27th Chinese Control and Decision Conference (CCDC)

作者： Zhao Xiaoqiang Xie Yaping Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Peoples R China

ISBN: (纸本)9781479970162

Aiming at complex data sets, affinity propagation clustering algorithm has shortcomings of clustering inefficiency and low accuracy. A semi-supervised affinity propagation clustering algorithm based on kernel function (K-SAP Clustering algorithm) is proposed in this paper. This algorithm first maps the complex clustering space into the feature space and change the similarity measure by a kernel function. Then semi-supervised algorithm is used to adjust the similarity matrix to be neighbours of data in same cluster. Finally, AP algorithm is used to iterate and update to get, the global optimum. Simulation results show the proposed algorithm is better and more accurate than SAP algorithm for complex data sets clustering.

关键词： Data Mining Clustering Kernel Fraction affinity propagation algorithm

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affinity propagation algorithm in WLAN network deployment

Affinity propagation algorithm in WLAN network deployment

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2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

作者： Wang, Yushun Lei, Peng Xu, Hong Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781479927166

The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the needs and guide the deployment of WLAN, that isn't a easy issue. It will waste the investment if we deploy the WLAN in improper places. This paper proposes a solution which can collect customer feedback with the help of smart phone client software and affinity propagation algorithm is applied to determine which region should be deploy first according to giant user feedback from that client software. Actual results show that the method is feasible and effective. It can significantly improve the accuracy of deployment of WLAN and efficiency of operations.

关键词： component WLAN Telecom operator mobile client affinity propagation algorithm clustering algorithm GIS

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Social welfare evaluation during demand response programs execution considering machine learning-based load profile clustering

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APPLIED ENERGY 2024年 357卷

作者： Moazzen, Farid Alikhani, Majid Aghaei, Jamshid Hossain, M. J. Univ Technol Sydney Sch Elect & Data Engn Ultimo Australia Cent Queensland Univ Sch Engn & Technol Rockhampton Australia

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the affinity propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model.

关键词： Demand response programs Smart grids affinity propagation algorithm Machine learning Load classification

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An improved density-based approach to risk assessment on railway investment

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DATA TECHNOLOGIES AND APPLICATIONS 2022年第3期56卷 382-408页

作者： Guo, Jingwei Zhang, Ji Zhang, Yongxiang Xu, Peijuan Li, Lutian Xie, Zhongqi Li, Qinglin Henan Polytech Univ Jiaozuo Henan Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China Changan Univ Xian Peoples R China

Purpose Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory. Design/methodology/approach First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China-Laos railway's investment risk assessment, and its performance is compared with other related algorithms. Findings The IM-DBSCAN algorithm is implemented on China-Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment. Originality/value This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to cla

关键词： Density-based clustering affinity propagation algorithm Rough set Risk assessment Railway investment

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Temporal-Structure-Aware Interference Cancellation for Asynchronous Cognitive IoT

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第2期11卷 293-297页

作者： Liu, Yi Yuan, Xiaojun Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China

In this letter, we investigate the interference cancellation problem for asynchronous cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model. An iterative receiver together with a new clustering algorithm is employed to cancel the interference and recovers the desired signal. Since the C-IoT device and the primary user (PU) are non-cooperative, they are generally asynchronous at the symbol level and the interference signal received by the secondary user (SU) receiver (Rx) possesses a first-order Markov property over time. Therefore, we propose a new clustering algorithm named hidden Morkov model (HMM) based affinity propagation (AP) algorithm that combines the HMM and the AP algorithm to realize the clustering module. The proposed HMM-AP algorithm exploits the temporal-correlation information to improve the performance of the iterative receiver. We show that the receiver with the HMM-AP algorithm can work well in non-cooperative system and has a much better performance than the original AP.

关键词： Interference Hidden Markov models Clustering algorithms Receivers Markov processes Synchronization Transmitters Machine learning non-coordination affinity propagation algorithm hidden Markov model

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Load Forecasting Method Based on SSA-DAP-TCN 2

Load Forecasting Method Based on SSA-DAP-TCN

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2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

作者： Yan, Lu Liu, Wei Liu, Han Wang, Xuechun Hua, Fan Cui, Yong Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China State Grid Shanghai Elect Power Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350303896

As the sampling frequency of electricity load increases, noise is difficult to filter at high sampling rates, which can lead to deviation in load forecasting and affect the performance of prediction algorithms. Enhancing the accuracy of load forecasting is crucial to formulate production plans, guide economic dispatch and maintain the balance of power supply and demand. This study proposes a power load forecasting method based on multi-scale decomposition and reconstruction. Initially, the load data is decomposed into trend, periodic, and noise components by singular spectrum analysis (SSA), effectively eliminating noise. Then, the affinity propagation algorithm based on dynamic time warping distance (DAP) is utilized to reconstruct the components to obtain new subsequences. Time convolutional network (TCN) is utilized to predict the reconstructed subsequences, and finally the forecasted results of each subsequence are accumulated to get the final forecasted results. To verify the superiority of the proposed method, this study take actual manufacturing industry load data as an example and comparing it with various prediction models. Compared with TCN, the root mean squared error of the proposed method is improved by 55.68%.

关键词： Singular spectrum analysis Dynamic time warping distance affinity propagation algorithm Time convolutional network Load forecasting

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