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检索条件"任意字段=2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022"

共 4131 条记录，以下是131-140 订阅

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Research on data mining and the short video platforms users' personalized information needs

Research on data mining and the short video platforms users'...

引用

2022 international conference on Electronic information Engineering and Computer Science, EIECS 2022

作者： Hao, Hongxu New York University ManhattanNY United States

ISBN: (纸本)9781510663312

The popularization and development of the Internet has prompted people to enter the era of informatization and digitization. With the development of new media technology and the influx of massive amounts of information, the problem between human information needs and information has changed from insufficient information to "useful" information acquisition difficulties. As a form of new media, short video has been favored by many people because of its unique characteristics since its emergence, but if the short video platform wants to get healthy and sustainable development, it needs to meet the personalized information needs of users to the greatest extent. At this time, the acquisition and analysis of users' personalized needs has become one of the focuses of the platform's work. Taking the short video platform as an example, this paper uses the K-means clustering analysis algorithm in data mining and the Apriori association rule mining algorithm to analyze the user's personalized information needs, and then according to the users requirements analysis, a user personalized information recommendation system for short video platforms - "KA-R" system is proposed to meet the purpose of meeting the personalized information needs of users. © 2023 SPIE.

关键词： K-means clustering

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Research on metadata management of data lake 3

Research on metadata management of data lake

引用

3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Guo, Zhixia Pan, Chenshuo Chang, Ke Li, Xinchi Cheng, Shasha Zhang, XinRui Research Institute of China Telecom Corporation Limited Beijing100000 China University of Wisconsin-Madison MadisonWI999039 United States Beijing Institute of Technology Beijing100000 China Beijing Normal University Beijing100000 China

ISBN: (纸本)9781510661363

Metadata management plays an important role in enterprise information management. The complete metadata management system has directly affected the flexibility and high scalability of the platform. This paper summarizes several important links in metadata management technology in data warehouses and large-scale distributed file systems. This paper organizes the metadata management standards, compares various metadata management architecture technologies,and summarizes the metadata management characteristics and metadata management strategies. At the same time, this paper introduces the applicability of the current mainstream open source metadata management tools in detail, focuses on the research of cognitive metadata directory based on machine learning, and prospects the future research direction. © COPYRIGHT SPIE.

关键词： information management

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An Optimized Association Rule data mining Algorithm 2

An Optimized Association Rule Data Mining Algorithm

引用

2nd European conference on Communication Systems, ECCS 2022

作者： Liu, Yanrong Wang, Lijun Shaanxi Institute of International Trade & Commerce College of Information Engineering Shaanxi China

ISBN: (数字)9781665496896

ISBN: (纸本)9781665496896

Among data mining algorithms, Apriori association rule data mining algorithm is one of the most widely used algorithms. The algorithm is faced with the problems such as low accuracy of algorithm recommendation, single support and difficulty in setting the support threshold in the operation of current e-commerce recommendation systems. Based on the above problems, this paper proposes an association rule data mining algorithm combining multi-item support tree and support. The algorithm uses recursive iteration to generate conditional database and dynamically adjust the minimum support during mining to obtain frequent item sets, which greatly improves the efficiency of data mining and the accuracy of recommendation. Experiments show that the proposed association rule data mining algorithm with multi-item support greatly changes the number of association rules, thus improving the efficiency of data mining and the accuracy of recommendation. © 2022 IEEE.

关键词： Association rules

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Temporal Linear Item-Item Model for Sequential Recommendation 25

Temporal Linear Item-Item Model for Sequential Recommendatio...

引用

18th international conference on Web Search and data mining-WSDM

作者： Park, Seongmin Yoon, Mincheol Choi, Minjin Lee, Jongwuk Sungkyunkwan Univ Suwon South Korea

ISBN: (纸本)9798400713293

In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. Linear SR models exhibit high efficiency and achieve competitive or superior accuracy compared to neural models. However, they solely deal with the sequential order of items (i.e., sequential information) and overlook the actual timestamp (i.e., temporal information). It is limited to effectively capturing various user preference drifts over time. To address this issue, we propose a novel linear SR model, named TemporAl LinEar item-item model (TALE), incorporating temporal information while preserving training/inference efficiency. It consists of three key components. (i) Single-target augmentation concentrates on a single target item, enabling us to learn the temporal correlation for the target item. (ii) Time interval-aware weighting utilizes the actual timestamp to discern the item correlation depending on time intervals. (iii) Trend-aware normalization reflects the dynamic shift of item popularity over time. Our empirical studies show that TALE outperforms ten competing SR models by up to 18.71% gains across five benchmark datasets. It also exhibits remarkable effectiveness for evaluating long-tail items by up to 30.45% gains. The source code is available at https://***/psm1206/TALE.

关键词： Sequential recommendation linear item-item models temporal information

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Business information mining technology of Social Media Platform Based on PageRank Algorithm 1

引用

6th European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid information Processing (ADHIP)

作者： Liu, Ying Jinggangshan Univ Sch Business Jian 343009 Peoples R China

ISBN: (数字)9783031288678

ISBN: (纸本)9783031288661;9783031288678

The existing methods of business information mining are flexible and cannot effectively mine the business information of media platform. In order to summarize and manage the mass business information effectively, a research method of business information mining based on PageRank algorithm for social media platform is proposed. Different from the existing methods, it innovatively optimizes the business information evaluation algorithm of social media platforms, increases the flexibility of information mining, and realizes the business information mining of social media platforms. The experiment proves that the technology of business information mining based on social media platforms can effectively summarize and manage a large amount of business information.

关键词： PageRank algorithm Social media Business information data mining

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Research of Drawing data Asset Graph Based on Force-Oriented Algorithm 21

Research of Drawing Data Asset Graph Based on Force-Oriented...

引用

21st international Computer conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Sihan, Huang Zhi, Qin Shibin, Zhang Yan, Chang Lili, Yan School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China

ISBN: (纸本)9798331519254

With the rapid development of the new energy vehicle industry, the data volume in the industry is growing explosively. How to effectively visualize and analyze these data has become a focus of attention in the industry. As an intuitive way of data organization and presentation, asset graphs can display complex data relationships in a graphical form, which helps to quickly understand the information behind the data. This paper proposes a method for drawing data asset graphs based on the force-oriented algorithm, specifically tailored to the data characteristics of the new energy vehicle industry. The method improves the existing force-oriented algorithms through feature-enhanced node importance assessment, customized force model construction, multi-level community division and clustering, and node layout optimization. Experimental verification has shown that this method can effectively visualize new energy vehicle data, with certain advantages in layout quality and layout efficiency. © 2024 IEEE.

关键词： Asset Graph data Visualization Force-Oriented Algorithm New Energy Vehicles

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Research on a New Electric Power Market Trading Strategy Based on Power Big data

Research on a New Electric Power Market Trading Strategy Bas...

引用

2022 international conference on Internet of Things and Machine Learning, IoTML 2022

作者： Wang, Tao Jia, Junqiang Xiao, Jingfeng Liu, Lulu Wang, Zhongjiao State Grid Xinjiang Information and Telecommunication Company Urumqi830000 China Harbin Space Star Data System Technology Co. Ltd. Harbin150028 China

ISBN: (纸本)9781510664913

The power industry has achieved rapid development with the strong support of national policies, which makes the relevant data information show geometric growth. With the further promotion of the power market reform, the market transaction subjects are gradually diversified. In this progress, a good trading strategy is particularly important in order to get more generous profits in the market trading progress. The application of power big data technology can provide security guarantee for electric power production and electric power transaction. Through data mining, data analysis, data extraction and data storage, it can provide a data basis for the development of various transactions in the electric power market. Based on the mining and processing of power big data, this paper builds a two-stage decision-making framework for the power market to study the trading strategies of market participants. The model established in this paper considers the uncertainty in the two-stage decision-making process, which can reflect the risks associated with the decision-making process. © 2023 SPIE.

关键词： Big data

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Application of Color Management System in BOM Configuration and Big data Analysis

Application of Color Management System in BOM Configuration ...

引用

IEEE international conference on Electrical Engineering, Big data and algorithms (EEBDA)

作者： Yu, Bohan North China Elect Power Univ Baoding Sch Econ & Management Baoding 071000 Peoples R China

ISBN: (纸本)9781665416061

Today, with the rapid development of information technology, the degree of enterprise information construction determines whether an enterprise can stand firm and develop at a high speed in the changeable business sea. BOM is the governance core of enterprise information construction, the foundation of any governance system and the main line running through various information systems. BOM governance is the main content of enterprise technology governance informatization. Moreover, in the whole product life cycle, different parts will have different requirements for BOM. These requirements can be mainly divided into design BOM EBOM, planned BOM pbom and manufacturing BOM EBOM.

关键词： BOM information technology

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Application of wind energy resource assessment system based on big data storage 5

Application of wind energy resource assessment system based ...

引用

5th international conference on Computer Vision and data mining, ICCVDM 2024

作者： Shi, Qi Lv, Guannan Ai, Bo Li, Benshuai Wang, Peipei Feng, Wenjun Shang, Hengshuai College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China Qingdao Yuehai Information Service Limited Qingdao266555 China Qilu Institute of Technology College of Civil Engineering Shandong Province Jinan273100 China

ISBN: (纸本)9781510682863

This paper constructs an interactive offshore wind power resource assessment system based on the 30-year data of ERA5 wind farm from the European Center for Medium-Range Weather Forecasts (ECMWF), combined with the offshore wind power big data storage technology of Apache Cassandra database and the wind energy resource assessment model, and following the current national standards and technical specification documents. In this paper, 6 wind turbines in Changyi wind farm are randomly selected for authenticity, reasonableness and completeness analysis and verification, which verifies the feasibility of constructing wind farms in this sea area, and provides scientific site selection basis and wind resource assessment reference for the construction of offshore wind farms in China. © 2024 SPIE.

关键词： Offshore wind farms

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Advancing Pulmonary Nodule Classification: A Novel Multi-Scale Fusion and Joint Upsampling Strategy using 3D Convolutional Neural Networks 23

Advancing Pulmonary Nodule Classification: A Novel Multi-Sca...

引用

2nd international conference on algorithms, data mining, and information technology, admit 2023

作者： Li, Jiajia Li J.J Guan, Junlin Guan J.L School of Computer and Information Security Guilin University of Electronic Science and Technology China

ISBN: (纸本)9798400707629

Lung cancer, renowned for having the highest global incidence and mortality rates among all cancers, presents a promising avenue for improving survival rates through early detection and precise diagnosis. However, current diagnostic methods relying on the manual interpretation of CT images are susceptible to subjectivity and potential errors. To address this challenge, we introduce an innovative fully convolutional neural network that synergistically integrates multi-scale feature fusion and joint upsampling modules. Our model aims to enhance the precision of lung cancer diagnosis by effectively categorizing benign and malignant pulmonary nodules within CT images. Leveraging a comprehensive dataset comprising 1012 pulmonary nodule samples sourced from LIDC-IDRI, our evaluation reveals exceptional performance metrics. Notably, the model achieves an impressive area under the ROC curve of 97.35%, along with a high accuracy of 94.21%, sensitivity of 93.79%, and specificity of 94.91%. © 2023 ACM.

关键词： Convolutional neural networks

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