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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

Generalization-based discovery of spatial association rules with linguistic cloud models

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Journal of Systems Engineering and Electronics 2004年第4期15卷 728-734,740页

作者： YangBin TianYongqing ZhuZhongying SchoolofElectronicandInformationTechnolcgy ShanghaiJiaotongUniversityShanghai200030P.R.China

Extraction of interesting and general spatial association rules from large spatial databases is an important task in the development of spatial database systems. In this paper, we investigate the generalization-based knowledge discovery mechanism that integrates attribute-oriented induction on nonspatial data and spatial merging and generalization on spatial data. Furthermore, we present linguistic cloud models for knowledge representation and uncertainty handling to enhance current generalization-based method. With these models, spatial and nonspatial attribute values are well generalized at higher-concept levels, allowing discovery of strong spatial association rules. Combining the cloud model based generalization method with Apriori algorithm for mining accociation rules from a spatial database shows the benefits in effectiveness and flexibility.

关键词： Pragmatics Spatial databases Clouds association rules Shape Generators

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.

Parallel mining and application of fuzzy association rules

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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 School of Electronic Information Wuhan University.Wuhan 430072Hubei China

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

Ethics Lines and Machine Learning: A Design and Simulation of an association rules Algorithm for Exploiting the Data

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Journal of Computer and Communications 2021年第12期9卷 17-37页

作者： Patrici Calvo Rebeca Egea-Moreno Department of Philosophy and Sociology Universitat Jaume I Castellon Spain Universitat Jaume I and Mirada TV Castellon Spain

Data mining techniques offer great opportunities for developing ethics lines whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate.

关键词： Data Mining Ethics Lines association rules Apriori Algorithm Company