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Building Simulation 2024年第7期17卷 1213-1229页

作者： Qiao Deng Zhiwen Chen Wanting Zhu Zefan Li Yifeng Yuan Weihua Gui School of Automation Central South UniversityChangsha 410083China Xiangjiang Laboratory of Hunan Province Changsha 410205China Shenzhen DAS Intellitech Co. Ltd.Shenzhen 518057China

Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in *** performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate *** overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association *** distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating *** graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of *** effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller *** analysis with state-of-the-art methods highlights the superior performance of the proposed method.

关键词： chillers performance prediction dynamic graph convolutional network association rules operating modes

A Matching Algorithm Based on association rules in Ontology Based Publish/Subscribe System

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Chinese Journal of Electronics 2015年第1期24卷 65-70页

作者： LIU Shufen CHI Meng YAO Zhilin College of Computer Science and Technology Jilin University

In this paper, we introduce association rules into the event matching process, and propose a matching algorithm based on association rules in ontologybased publish/subscribe system. The algorithm discovers association rules from subscriptions, and then integrates these association rules into the arrived events. These integrated association rules guide the event matching process,which can greatly improve efficiency by reducing unnecessary matching processes. To solve the available problem of integrating the association rules into the ontology-based publish/subscribe system, we also show an approach of transforming the subscriptions into a format that can apply the data mining algorithm and an approach of integrating the association rule into the events. The results show that the algorithm achieves performance improvement of matching efficiency by using comparison.

关键词： association rules Ontology matching Publish/subscribe system.

Mining Frequent Sets Using Fuzzy Multiple-Level association rules

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Journal of Electronic Science and Technology 2018年第2期16卷 145-152页

作者： Qiang Gao Feng-Li Zhang Run-Jin Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.

关键词： association rules fuzzy multiple-level association(FMA) rules algorithm fuzzy set improved Eclat algorithm

Mining association rules in incomplete information systems

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Journal of Central South University of Technology 2008年第5期15卷 733-737页

作者：罗可王丽丽童小娇 School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410076 China School of Computer and Communication Engineering Changsha University of Science and Technology Department of Computer Science and Technology Dezhou University Dezhou 253023 China

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

关键词： association rules rough sets prediction support prediction confidence incomplete information system

Regression Analysis of the Number of association rules

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International Journal of Automation and computing 2011年第1期8卷 78-82页

作者： Wei-Guo Yi Ming-Yu Lu Zhi Liu Department of Information Science and Technology Dalian Maritime University Dalian 116026 PRC Department of Software Institute Dalian Jiaotong University Dalian 116052 PRC

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

关键词： association rules regression analysis multiple correlation coeficients interest support confidence.

Study on the construction of symptom association model based on association rules

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Study on the construction of symptom association model based...

Parallel mining and application of fuzzy association rules

第36届中国控制会议

作者： Zhiwan Wang Jiansheng Li Chuxiang Chen Liang Zhang Xiang Wang Xiaobo Yang Haixia Li Ling Li The First Affiliated Hospital of Henan University of TCM Collaborative Innovation Center for Respiratory Disease Diagnosis and Treatment & Chinese Medicine Development of Henan Province Zhengzhou Institute of Information Science and Technology Zhengzhou University of Aeronautics

ISBN: (纸本)9781538629185

With complex pathogenesis,Chronic Obstructive Pulmonary Disease（COPD） is difficult to *** Chinese Medicine（TCM） showed obvious effect in treating ***,invaluable TCM experience lacks of systematic summarization and *** rule is used to discover the relationships among data items in a large amount of *** of clear and useful results,association rule is widely used in study of TCM *** this paper,association model of symptom of phlegm-heat obstructing lung in acute exacerbation COPD is constructed.

关键词： association rules Chronic Obstructive Pulmonary Disease(COPD) acute exacerbations the syndrome of phlegm-heat obstructing lung symptoms

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Frontiers of Electrical and Electronic Engineering in China 2006年第2期1卷 177-182页

作者： LU Jian-jiang XU Bao-wen ZOU Xiao-feng KANG Da-zhou LI Yan-hui ZHOU Jin Institute of Command Automation PLA University of Science and TechnologyNanjing 210007China Department of Computer Science and Engineering Southeast UniversityNanjing 210096China

Quantitative attributes are partitioned into several fuzzy sets by using fuzzy c-means *** c-means algorithm can embody the actual distribution of the data,and fuzzy sets can soften the partition ***,we improve the search technology of apriori algorithm and present the algorithm for mining fuzzy association *** the database size becomes larger and larger,a better way is to mine fuzzy association rules in *** the parallel mining algorithm,quantitative attributes are partitioned into several fuzzy sets by using parallel fuzzy c-means *** parallel algorithm is improved to discover frequent fuzzy attribute set,and the fuzzy association rules with at least a minimum confidence are generated on all *** experiment results implemented on the distributed linked PC/workstation show that the parallel mining algorithm has fine scaleup,sizeup and ***,we discuss the application of fuzzy association rules in the *** example shows that the accuracy of classification systems of the fuzzy association rules is better than that of the two popular classification methods:C4.5 and CBA.

关键词： Data mining association rules Fuzzy Parallel Classification

An Object Extraction Model Using association rules and Dependence Analysis

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 405-409页

作者： Li Shen zhi 1, Chen Zhen qiang 1, Zhou Yu ming 1,Xu Bao wen 1,2 1. Department of Computer Science and Engineering, Southeast University, Nanjing 210096,China 2. State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072,Ch Department of Computer Science and Engineering Southeast University Nanjing China State Key Laboratory of Software Engineering Wuhan University Wuhan China

Extracting objects from legacy systems is a basic step in system's object orientation to improve the maintainability and understandability of the systems. A new object extraction model using association rules and dependence analysis is proposed. In this model data are classified by association rules and the corresponding operations are partitioned by dependence analysis.

关键词： reverse engineering data mining association rules dependence analysis program slicing

association rules Applied to Intrusion Detection

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Wuhan University Journal of Natural Sciences 2002年第4期7卷 426-430页

作者： Mao Ping-ping Zhu Qiu-ping Wuhan University School of Electronic Information Wuhan Hubei China (GRID:grid.49470.3e) (ISNI:***)

We discuss the basic intrusion detection techniques, and focus on how to apply association rules to intrusion detection. Begin with analyzing some close relations between user’s behaviors, we discuss the mining algorithm of association rules and apply to detect anomaly in IDS. Moreover, according to the characteristic of intrusion detection, we optimize the mining algorithm of association rules, and use fuzzy logic to improve the system performance.

关键词： Intrusion detection association rules fuzzy logic

Words alignment based on association rules for cross-domain sentiment classification

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Frontiers of Information Technology & Electronic Engineering 2018年第2期19卷 260-272页

作者： Xi-bin JIA Ya JIN Ning LI Xing SU Barry CARDIFF Bir BHANU Faculty of Information Technology Beijing University of Technology School of Electrical and Electronic Engineering University College Dublin Visualization and Intelligent Systems Laboratory University of CaliforniaRiverside

Automatic classification of sentiment data（e.g., reviews, blogs） has many applications in enterprise user management systems, and can help us understand people＇s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules（WAAR） for cross-domain sentiment classification,which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon~ datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.

关键词： Sentiment classification Cross-domain association rules