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E-Commerce Precision Marketing and Consumer Behavior Models Based on IoT clustering algorithm

JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2022年第5期24卷 1-21页

作者： Guo, Shujuan Zhai, Rongbing Anhui Xinhua Univ Business Sch Hefei Peoples R China

This article aims to study e-commerce precision models and consumer behavior models based on clustering algorithms, and at the same time conduct detailed research on the Gaussian mixture distribution algorithm, consumer behavior and model construction, and precision marketing strategies in the clustering algorithm. First, a lot of analysis and demonstration of precision marketing strategies and the construction of consumer behavior models are carried out, and then the clustering algorithm -based electronic some experiments were carried out on the application of commercial precision marketing methods and consumer behavior models. The experimental results show that the precision marketing method using the clustering algorithm is more in line with the development of modern e-commerce. The application of the algorithm in the precision marketing methods of enterprises and consumer behavior models has promoted the vigorous development of enterprises, making the sales volume of enterprises reach 9.8%.

关键词： Behavior Model clustering algorithm E-Commerce Precision Marketing Methods

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B2C E-Commerce Logistics Network Optimization Based on Constrained clustering algorithm 4

B2C E-Commerce Logistics Network Optimization Based on Const...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Xiaoliang, Xiong Wuhan Technologe and Business University Hubei 430065 China

ISBN: (纸本)9798350305258

With the improvement of residents' consumption level, attention to quality has increasingly become an important reference standard for consumers to choose e-commerce, making e-commerce gradually shift from "price war" to "service war". The research on B2C e-commerce logistics network optimization based on constrained clustering algorithm is a research work in the field of information technology and engineering. The main goal of this research is to develop a new algorithm to optimize the performance of B2C e-commerce logistics network. This research also focuses on developing an effective method to identify the constraints that affect the performance of B2C e-commerce logistics network. In order to optimize the performance of B2C e-commerce logistics network, constraint clustering method is used to identify all constraints that affect the performance of B. In B2C e-commerce logistics network optimization, constrained clustering algorithms can also optimize delivery paths. By preset constraints such as starting point, ending point, and necessary passing point, constrained clustering algorithms can optimize delivery routes, reduce transportation time and costs while ensuring the same quality of consumer service. In addition, constrained clustering algorithms can also predict logistics demand, formulate more reasonable logistics plans, and dynamically adjust according to actual situations. © 2023 IEEE.

关键词： B2C e-commerce clustering algorithm Logistics network

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Research on Fast and Corresponding Multi-Stream Data Management System of Power Pretraining Based on Central Weighted Data Stream clustering algorithm 3

Research on Fast and Corresponding Multi-Stream Data Managem...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Liu, Han Chen, Xia Guo, Qing Yu, Jiangbin Liu, Xiaofei State Grid Shandong Electric Power Company Beijing China State Grid Information and Communication Industry Group Co. Ltd Jinan China

ISBN: (纸本)9798350341546

To solve the anomaly detection problem of power big data stream, this paper combines the clustering algorithm of power big data stream with power big data stream. In view of the existing stream data clustering algorithm is not easy to store all the data, power data easy to lose and other problems and the stream data clustering algorithm for offline stage clustering algorithm real-time response requirements from the data integrity, security and low time complexity of the stream data clustering algorithm to improve the CLU Stream data clustering algorithm. At the same time, a central-weighted data stream clustering algorithm is proposed. In the online phase, Redis cluster is used to buffer stream data, and node time attenuation strategy is designed. Increases the percentage of valid heartbeat messages. The offline stage clustering algorithm is optimized. The optimal distance method is used to determine the initial clustering center and reduce the number of iterations. Finally, the proposed central-weighted data stream clustering algorithm is used to detect the abnormal behavior of users. The experimental results show that the algorithm can detect the abnormal behavior of users well. © 2023 IEEE.

关键词： Center weighting clustering algorithm multi-stream data management system power data

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Design of Multi-source Big Data Cross Source Scheduling System Based on Optimal K-means clustering algorithm

Design of Multi-source Big Data Cross Source Scheduling Syst...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Gong, Lili Zhang, Guang Sun, Xiang Liaoning Vocational College of Economics Shenyang China No.12 Middle School of Wuhan Hubei China

ISBN: (纸本)9798350313413

Traditional big data scheduling methods cannot effectively integrate massive data in the network, which easily leads to 'rigid' operation of the system. In response to the above issues, this project intends to study a multi-source collaborative scheduling method for multi-source heterogeneous big data. K-average connections store data in binary format, and mine the index nodes of the data to achieve organizational indexing of the data. On this basis, the scheduling framework was used to obtain cross source parameters, and the location of the cross source scheduling center was determined through direct tracking, achieving the smooth application of the design of a multi-source big data cross source scheduling system based on the optimal K-means clustering algorithm. The scheduling time of the Design System 3 is relatively long, but its scheduling time does not change much as the amount of data increases. Through simulation and comparison, the experimental results showed that compared with traditional big data scheduling methods, the new scheduling method can effectively suppress the phenomenon of system rigid operation. © 2023 IEEE.

关键词： clustering algorithm Cross Source Scheduling Multi-source Big Data Optimal K-means

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Analysis of Network Marketing Performance Evaluation System Based on BP Neural Network clustering algorithm

Analysis of Network Marketing Performance Evaluation System ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Quan, Xiaoyan Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350315455

With the rapid development of Internet technology, corporate marketing has become an essential way for the survival of companies in the market. Improving corporate marketing and providing support for companies effectively is extremely important. In response to these needs, this paper attempts to introduce the BP neural network clustering algorithm, through the construction of a three-layer index system for enterprise network marketing, to clarify the specific set of enterprise network marketing indexes;on this basis, through the BP neural network clustering method, enterprise network marketing is carried out. Comprehensive multi-level fuzzy evaluation of performance. Finally, the actual enterprise network marketing evaluation is conducted according to the specific calculation of index calculation. The results of the simulation experiment show that 0.435 of estimated vale, 0.096 of standard deviation, and 4.545 of Z-value of the BP neural network clustering algorithm is effective and can support enterprise network marketing performance evaluation and analysis. © 2023 IEEE.

关键词： BP Neural Network clustering algorithm Enterprise Multi-level fuzzy Network Marketing Performance Evaluation

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An improved LBG clustering algorithm based on Bayesian information criterion 14

An improved LBG clustering algorithm based on Bayesian infor...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Wan, Sisi Wu, Gongyu Department of Network Security No. 30 Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China Department of Generic Technology Research Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9798350301359

Cluster analysis is an unsupervised classification method, which is the pretreatment part in data mining. And It is also the key to discovering unknown knowledge. However, the traditional clustering algorithm has some problems such as some algorithms need to predetermine k values, some algorithms can spontaneously search for k values but are not accurate enough and some algorithms can accurately search k values but the computational efficiency is too low. In order to solve the challenge of traditional clustering algorithm. An improved Linde-Buzo-Gray (LBG) clustering algorithm based on Bayesian information criterion was proposed. First, according to the traditional LBG thoughts to cluster and the rough value of k is obtained. After that the Bayesian information criterion was used to optimize each cluster and the exact k value was obtained. The theoretical analysis and simulation results show that the improved algorithm can search for the correct k value with high efficiency for clustering, which is more accurate than several traditional clustering methods. © 2023 IEEE.

关键词： Bayesian information criterion clustering algorithm Data mining knowledge discovery LBG algorithm

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Novel Adjustable Potential Assessing Method of Customer Loads Based on the clustering algorithm of Electricity Consumption Patterns 3

Novel Adjustable Potential Assessing Method of Customer Load...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Luo, Zhe Cui, Jia Yang, Zhiwei Gao, Mingze Hu, Zhen Tian, Aixin Shenyang University of Technology School of Electrical Engineering Liaoning Shenyang China State Grid Jibei Electric Power Company Limited Beijing China

ISBN: (纸本)9798350393224

Tapping the regulation resources on the 'load' side, upgrading the traditional source-load movement to source-load interaction, and improving the regulation capability of the power system are the new and important way to ensure the safe and economic operation of the new power system. This paper proposes a method for assessing the adjustable potential of users based on the clustering algorithm of power usage patterns. Firstly, the regularity of user electricity consumption and the classification method of electricity consumption pattern are studied. The abnormal data is excluded from the original data of user load. And the KNN algorithm is utilized to compensate the load data to ensure the integrity of the data. Secondly, the K-Means big data analysis algorithm is proposed to get the typical electricity consumption pattern of users. Thirdly, the baseline load on the regulation day is classified based on the affiliation function. Finally, based on the feasibility of the conversion of different power usage patterns, the calculated average error between the most likely regulation power of users and the actual value provides adjustable power data support for flexible load aggregation regulation. © 2023 IEEE.

关键词： adjustable potential assessment clustering algorithm power usage pattern

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Sensor Fault Diagnosis of Air Conditioning System Based on Multi-dimension clustering algorithm 1

Sensor Fault Diagnosis of Air Conditioning System Based on M...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Liu, Yuan Zhou, Haitao Li, Zepeng Mei, Lin Cai, Di Hu, Jian Information Communication Company Zhengzhou State Grid Henan Electric Power Corporation Henan China

ISBN: (纸本)9798350316469

With the development of modern architecture, indoor refrigeration and air conditioning system has become more and more complex. In these complex systems, the ability to accurately and timely detect and diagnose multiple faults is beyond human ability, so fault detection and diagnosis becomes more and more important. The purpose of this paper is to study sensor fault diagnosis of air conditioning system based on multi - dimensional clustering algorithm. This paper describes the basic theory of clustering algorithm and the basic process of sensor fault diagnosis in air conditioning system. Taking the laboratory air conditioning system as the experimental data object, multi-condition modeling data were identified and classified by multi-dimension Gaussian mixture model EM clustering algorithm. By selecting TPR statistics as the test measure, the feasibility and rationality of the strategy are verified. © 2023 IEEE.

关键词： Air Conditioning System clustering algorithm Fault Diagnosis Multidimensional clustering Sensor Fault

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Customer Market Analysis Based on Interval Value Data Dynamic clustering algorithm

Customer Market Analysis Based on Interval Value Data Dynami...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Hu, Yihan University College London London United Kingdom

ISBN: (纸本)9798350315455

The traditional K-means clustering center initial value algorithm does not perform well in massive data. In order to improve the initialization efficiency of the clustering center, this article adopted the idea of dynamic clustering algorithm for inter region value data to classify the data, and selected the center of dense grids as the initial clustering center based on the distribution characteristics of the data. This method can ensure that the cluster center is located in a high-density area rather than being overly concentrated, allowing the K-Means algorithm to quickly and efficiently initialize the cluster center when processing massive data. Finally, this article compared and analyzed the performance of data clustering methods using traditional K-means algorithm and K-means++algorithm through simulation experiments. The evaluation indicators of the experiment are CR index, Silhouette Score, and accuracy. From the comprehensive analysis of three indicators, it can be seen that the performance of customer market analysis data based on this algorithm was superior to other algorithms (Silhouette Score: this algorithm was 0.1249 higher than K means++and 0.4903 higher than K-means algorithm). The results indicated that compared with traditional clustering methods, clustering methods had significant advantages in the clustering process. © 2023 IEEE.

关键词： clustering algorithm customer market analysis interval value data dynamics K-means algorithm

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KDM-OPTICS based vehicle clustering algorithm for traffic millimeter-wave radar

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第5期36卷 056306-056306页

作者： Tian, Feng Wang, Siyuan Fu, Weibo Wei, Tianyu Yang, Jiqing Xian Univ Sci & Technol Coll Commun & Informat Technol Xian 710054 Peoples R China Xian Key Lab Network Convergence Commun Xian 710054 Peoples R China

Traffic millimeter-wave radar suffers from environmental noise, dynamic changes in the number of vehicles, and feature similarity leading to degradation of point-trace clustering accuracy when measuring moving vehicle targets. We propose a new density-based clustering algorithm to address this problem. Unlike traditional datasets, the radar measurement dataset used in this algorithm contains specific multidimensional information against a moving vehicle target, such as target distance, normal angle, relative velocity, and reflection intensity. In order to eliminate the correlation interference between the parameters as well as the influence of measurement units, the normalized Mahalanobis distance method is introduced to generate the target spot trace distance matrix. Subsequently, based on the target point trace distance matrix, the reachable distance map is quickly generated by KD tree nearest neighbor search. At the same time, the dynamic adjustment of neighborhood radius of OPTICS is introduced to enhance the contrast of the target point traces in the reachable distance matrix, to obtain the point trace segmentation data of similar vehicles,and to realize the point trace clustering of vehicle targets in the distance and speed dimensions. Finally,the target ranking recognition clustering structure algorithm (KDM-OPTICS) based on KD tree and normalized martens distance is *** this study,experimental validation based on vehicle target data from real urban traffic scenarios is carried *** results show that enhancing the target information by local power-law transformation can improve the stability and reliability of distinguishing vehicles with similar distance speed. In addition,the KDM-OPTICS algorithm has higher recognition accuracy and lower computational speed compared with other algorithms.

关键词： millimeter-wave radar vehicle detection clustering algorithm mahalanobis distance OPTICS power-law transformation KD tree

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