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Parallel frequent pattern Discovery:Challenges and Methodology

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Tsinghua Science and Technology 2007年第6期12卷 719-728页

作者：张宇宙王建勇周立柱 Department of Computer Science and Technology Tsinghua University

Parallel frequent pattern discovery algorithms exploit parallel and distributed computing resources to relieve the sequential bottlenecks of current frequent pattern mining （FPM） algorithms. Thus, parallel FPM algorithms achieve better scalability and performance, so they are attracting much attention in the data mining research community. This paper presents a comprehensive survey of the state-of-the-art parallel and distributed frequent pattern mining algorithms with more emphasis on pattern discovery from complex data （e.g., sequences and graphs） on various platforms. A review of typical parallel FPM algorithms uncovers the major challenges, methodologies, and research problems in the field of parallel frequent pattern discovery, such as work-load balancing, finding good data layouts, and data decomposition. This survey also indicates a dramatic shift of the research interest in the field from the simple parallel frequent itemset mining on traditional parallel and distributed platforms to parallel pattern mining of more complex data on emerging architectures, such as multi-core systems and the increasingly mature grid infrastructure.

关键词： frequent pattern mining parallel computing dynamic load balancing

维普期刊数据库

Stage Division and pattern Discovery of Complex Patient Care Processes

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Journal of Systems Science & Complexity 2017年第5期30卷 1136-1159页

作者： WANG Tingyan TIAN Xin YU Ming QI Xin YANG Lan Health Care Services Research Center Department of Industrial EngineeringTsinghua UniversityBeijing 100084China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Health Care Serv-ices Research Center Department of Industrial EngineeringTsinghua UniversityBeijing 100084China

This paper studies the design of a clinical pathway that defines medical service activities within each stage of a patient care process. Much prior research has developed clinicM process models that consider the trajectory of services occurring in a care process, by using data mining techniques on process execution logs. A novel approach that provides a more efficient way of clinical pathway design is introduced in this paper. Based on the strategy of TEI@I methodology, the proposed approach integrates statistical methods, optimization techniques and data mining. With the preprocessed data, a complex care process is subsequently divided into several medical stages, and then the patterns of each stage are identified, and thus a clinical pathway is developed. Finally, the proposed method is applied to the real world, using archival data derived from a hospital in Beijing, about three diseases that involve various departments, with an average of 300 samples for each disease. The results of real- world applications demonstrate that the proposed method can automatically and efficiently facilitate clinical pathways design. The main contributions to the field in this paper include （a） a new application of TEI@I methodology in healthcare domain, （b） a novel method for complex processes analysis, （c） tangible evidence of automatic clinical pathways design in the real world.

关键词： Clinical pathway frequent pattern mining stage division TEI@I virtual business.

维普期刊数据库

Efficient Incremental Maintenance of frequent patterns with FP-Tree

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Journal of Computer Science & Technology 2004年第6期19卷 876-884页

作者： Xiu-LiMa Yun-HaiTong Shi-WeiTang Dong-QingYang SchoolofElectronicsEngineeringandComputerScience PekingUniversityBeijing100871P.R.China NationalLaboratoryonMachinePerception PekingUniversityBeijing100871P.R.China

mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, efficient maintenance of the discovered patterns is crucial. Most existing methods need to scan the entire database repeatedly, which is an obvious disadvantage. In this paper, an efficient incremental mining algorithm, Incremental-mining (IM), is proposed for maintenance of the frequent patterns when incremental data come. Based on the frequent pattern tree (FP-tree) structure, IM gives a way to make the most of the things from the previous mining process, and requires scanning the original data once at most. Furthermore, IM can identify directly the differential set of frequent patterns, which may be more informative to users. Moreover, IM can deal with changing thresholds as well as changing data, thus provide a full maintenance scheme. IM has been implemented and the performance study shows it outperforms three other incremental algorithms: FUP, DB-tree and re-running frequent pattern growth (FP-growth). Keywords data mining - association rule mining - frequent pattern mining - incremental mining Supported by the National Basic Research 973 Program of China under Grant No.G1999032705.Xiu-Li Ma received the Ph.D. degree in computer science from Peking University in 2003. She is currently a postdoctoral researcher at National Lab on Machine Perception of Peking University. Her main research interests include data warehousing, data mining, intelligent online analysis, and sensor network.Yun-Hai Tong received the Ph.D. degree in computer software from Peking University in 2002. He is currently an assistant professor at School of Electronics Engineering and Computer Science of Peking University. His research interests include data warehousing, online analysis processing and data mining.Shi-Wei Tang received the B.S. degree in mathematics from Peki

关键词： data mining association rule mining frequent pattern mining incremental mining

Discovering top-k patterns with differential privacy-an accurate approach

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Frontiers of Computer Science 2014年第5期8卷 816-827页

作者： Xiaojian ZHANG Xiaofeng MENG School of Information Renmin University of China Beijing 100872 China School of Computer and Information Engineering Henan University of Economics and Law Zhengzhou 450002 China

frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data （e.g., user behavior records, electronic health records）, directly releas- ing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this pa- per, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern （DFP- Growth）, that integrates a Laplace mechanism and an expo- nential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is （λ, δ）-useful and differ- entially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.

关键词： frequent pattern mining differential privacy,constrained inference.

Hybrid Recommender System Using Systolic Tree for pattern mining

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Computer Systems Science & Engineering 2023年第2期44卷 1251-1262页

作者： S.Rajalakshmi K.R.Santha Department of Computer Science Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudurIndia

A recommender system is an approach performed by e-commerce for increasing smooth users’*** pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of *** work will present the implementation of sequence pattern mining for recommender systems within the domain of *** work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender *** feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target *** will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy *** work pre-sents a new hybrid recommender system based on optimized feature selection and systolic *** features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)*** systolic tree is used for pattern mining,and based on this,the recommendations are *** proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes conﬁrmed the efﬁciency of the *** was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativeﬁltering,the precision of 0.89 was achieved.

关键词： Recommender systems hybrid recommender systems frequent pattern mining collaborativeﬁltering systolic tree river formation dynamics particle swarm optimization

mining Software Repository for Cleaning Bugs Using Data mining Technique

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Computers, Materials & Continua 2021年第10期69卷 873-893页

作者： Nasir Mahmood Yaser Hafeez Khalid Iqbal Shariq Hussain Muhammad Aqib Muhammad Jamal Oh-Young Song University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Mathematics and Statistics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Sejong UniversitySeoul05006Korea

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and ***,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in ***,there is a need for a repository mining technique for relevant and bug-free data *** paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality *** predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation ***,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure *** evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial *** evaluation showed that the results of our proposal are *** and developers can utilize these rules for defect prediction during early software development.

关键词： Fault prediction association rule data mining frequent pattern mining

Adaptive associative classification with emerging frequent patterns

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High Technology Letters 2012年第1期18卷 38-44页

作者： Wang Xiaofeng Zhang Dapeng Shi Zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China Graduate University of Chinese Academy of Sciences Beijing 100049P.R.China Institute of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China

In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.

关键词： associative classification rule frequent pattern mining emerging frequent pattern supportvector machine (SVM)

Improving cis-regulatory elements modeling by consensus scaffolded mixture models

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Science China(Information Sciences) 2013年第1期56卷 219-229页

作者： JIANG HongShan ZHAO Ying CHEN WenGuang ZHANG XueGong Department of Computer Science and Technology Tsinghua University MOE Key Laboratory of Bioinformatics and the Bioinformatics Division Department of AutomationTsinghua University

A position weight matrix(PWM) is widely accepted as a probabilistic representation for modeling protein-DNA binding *** studies showed that for factors which bind to divergent binding sites,mixtures of multiple PWMs improve *** propose a consensus scaffolded mixutre PWM(CSM) model to improve cis-regulatory elements modeling by allowing overlapping components represented by a set of PWMs,each of which corresponds to a binding pattern and is scaffolded by a degenerate *** addition,we propose a learning algorithm that involves an initial structure learning stage based on the frequent pattern mining and a refining stage based on the expectation maximization(EM) *** assess the merits of CSM using three independent *** a case-study of transcription factor Leu3,the derived CSM models agree with conventional mixtures but show better fitness according to Fermi-Dirac *** of the human-mouse conservation of predicted binding sites of 83 JASPAR transcription factors(TFs) shows that the CSM is as good as or better than the simple mixture,the context-specific independent(CSI) mixture,and the single PWM model,for 83%,84%,and 75% of the cases,***-fold cross validation on 46 TRANSFAC datasets shows that CSM model has better generality than other mixture models.

关键词： position weight matrix cis-regulatory elements mixture models frequent pattern mining EM algorithm