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Fuzzy comprehensive evaluation of physical education based on high dimensional data mining

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 3065-3076页

作者： Wang, Zhihui Inner Mongolia Univ Nationalities Coll Phys Educ Tongliao 028043 Peoples R China

The spectrum cluster algorithm is opposite in the other cluster algorithm has the obvious superiority that can distinguish the non-raised distribution the cluster, suits extremely in many actual problems. With this prior model this paper combines the fuzzy set system to propose the new data mining algorithm. At the same time, we build a fuzzy comprehensive evaluation model and analyze the evaluation system of physical education. The result shows that the fuzzy comprehensive evaluation method is improved by using the grey relational grade, which avoids the disadvantages of adopting the principle of maximum membership degree. Therefore, this evaluation method can be used in the evaluation of public sports teaching in colleges and universities.

关键词： mining algorithm grey system public physical education high dimensional data fuzzy comprehensive evaluation

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mining High Utility Itemsets Using Prefix Trees and Utility Vectors

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第10期35卷 10224-10236页

作者： Qu, Jun-Feng Fournier-Viger, Philippe Liu, Mengchi Hang, Bo Hu, Chunyang Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Hubei Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518055 Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Key Lab Big Data & Intelligent Educ Guangzhou 510631 Guangdong Peoples R China

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. mining high utility itemsets in a database with n items generally results in a huge search space, composed of 2(n )itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance algorithm for mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

关键词： High utility itemset mining algorithm prefix tree utility vector

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mining dynamic association rules with comments

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KNOWLEDGE AND INFORMATION SYSTEMS 2010年第1期23卷 73-98页

作者： Shen, Bin Yao, Min Wu, Zhaohui Gao, Yunjun Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Zhejiang Univ Ningbo Inst Technol Ningbo 315100 Zhejiang Peoples R China Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

In this paper, we study a new problem of mining dynamic association rules with comments (DAR-C for short). A DAR-C contains not only rule itself, but also its comments that specify when to apply the rule. In order to formalize this problem, we first present the expression method of candidate effective time slots, and then propose several definitions concerning DAR-C. Subsequently, two algorithms, namely ITS2 and EFP-Growth2, are developed for handling the problem of mining DAR-C. In particular, ITS2 is an improved two-stage dynamic association rule mining algorithm, while EFP-Growth2 is based on the EFP-tree structure and is suitable for mining high-density mass data. Extensive experimental results demonstrate that the efficiency and scalability of our proposed two algorithms (i.e., ITS2 and EFP-Growth2) on DAR-C mining tasks, and their practicability on real retail dataset.

关键词： Dynamic association rule Comment Support vector Confidence vector mining algorithm

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Combined data mining techniques based patient data outlier detection for healthcare safety

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International Journal of Intelligent Computing and Cybernetics 2016年第1期9卷 42-68页

作者： Gebeyehu Belay Gebremeskel Chai Yi Zhongshi He Dawit Haile State Key Laboratory of Power Transmission Equipment System Security and New TechnologyCollege of AutomationChongqing UniversityChongqingChina College of Computer Science Chongqing UniversityChongqingChina School of Engineering Science and TechnologyVirginia State UniversityPetersburgVirginiaUSA

Purpose–Among the growing number of data mining(DM)techniques,outlier detection has gained importance in many applications and also attracted much attention in recent *** the past,outlier detection researched papers appeared in a safety care that can view as searching for the needles in the ***,outliers are not always ***,the purpose of this paper is to investigate the role of outliers in healthcare services in general and patient safety care,in ***/methodology/approach–It is a combined DM(clustering and the nearest neighbor)technique for outliers’detection,which provides a clear understanding and meaningful insights to visualize the data behaviors for healthcare *** outcomes or the knowledge implicit is vitally essential to a proper clinicaldecision-making *** method isimportant to thesemantic,andthe novel tactic of patients’events and situations prove that play a significant role in the process of patient care safety and ***–The outcomes of the paper is discussing a novel and integrated methodology,which can be inferring for different biological data *** is discussed as integrated DM techniques to optimize its performancein the field of health and *** is an integrated method of outliers detection that can be extending for searching valuable information and knowledge implicit based on selected patient *** on these facts,outliers are detected as clusters and point events,and novel ideas proposed to empower clinical services in consideration of customers’*** is also essential to be a baseline for further healthcare strategic development and research *** limitations/implications–This paper mainly focussed on outliers *** isolation that are essential to investigate the reason how it happened and communications how to mitigate it did not ***,the research can be extended more about the hierarchy of patient prob

关键词： Data mining Clustering Healthcare mining algorithm Nearest neighbor Outlier detection

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Microbial genotype-phenotype mapping by class association rule mining

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BIOINFORMATICS 2008年第13期24卷 1523-1529页

作者： Tamura, Makio D'haeseleer, Patrik Lawrence Livermore Natl Lab Comp Applicat & Res Dept Chem Mat Earth & Life Sci Dept Microbial Syst Biol Grp Livermore CA 94550 USA

Motivation: Microbial phenotypes are typically due to the concerted action of multiple gene functions, yet the presence of each gene may have only a weak correlation with the observed phenotype. Hence, it may be more appropriate to examine co-occurrence between sets of genes and a phenotype (multiple-to-one) instead of pairwise relations between a single gene and the phenotype. Here, we propose an efficient class association rule mining algorithm, NETCAR, in order to extract sets of COGs (clusters of orthologous groups of proteins) associated with a phenotype from COG phylogenetic profiles and a phenotype profile. NETCAR takes into account the phylogenetic co-occurrence graph between COGs to restrict hypothesis space, and uses mutual information to evaluate the biconditional relation. Results: We examined the mining capability of pairwise and multiple-to-one association by using NETCAR to extract COGs relevant to six microbial phenotypes (aerobic, anaerobic, facultative, endospore, motility and Gram negative) from 11 969 unique COG profiles across 155 prokaryotic organisms. With the same level of false discovery rate, multiple-to-one association can extract about 10 times more relevant COGs than one-to-one association. We also reveal various topologies of association networks among COGs (modules) from extracted multiple-to-one correlation rules relevant with the six phenotypes;including a well-connected network for motility, a star-shaped network for aerobic and intermediate topologies for the other phenotypes. NETCAR outperforms a standard CAR mining algorithm, CARAPRIORI, while requiring several orders of magnitude less computational time for extracting 3-COG sets.

关键词： .graph anaerobic mutual information Co-occurrence COGs mining algorithm D’haeseleer Microbial genotype–phenotype mapping multiple-to-one NETCAR

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mining Decision Activity Logs

Mining Decision Activity Logs

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13th International Conference on Business Information Systems

作者： Petrusel, Razvan Mican, Daniel Univ Babes Bolyai Fac Econ Sci & Business Adm Cluj Napoca 400591 Romania

ISBN: (纸本)9783642154010

This paper introduces our work regarding the mining of decision activity logs generated by the users of a decision support system-like environment. We will show that a DSS can be modified in order to become "decision-aware. If the system offers support for all the data and information needs of the decision maker, how the user interacts with the software can provide us with a new perspective over the implicit and explicit knowledge employed in the decision process, as well as the decision patterns and strategies used for that decisional situation. All this valuable information will be stored as activity logs. Those logs need to be mined in order to build a graphical representation of the decision process. As proof-of-concept we focus on the enterprise loan contracting decision situation. We will show some of the models we created using several process mining algorithms and our own approach. Based on those models, we argue the new insights we can provide into the decision making process and the knowledge that is now explained and depicted as diagrams.

关键词： decision mining user activity log process mining mining algorithm decision reference model

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Static mining and Dynamic Taint for Mobile Security Threats Analysis

Static Mining and Dynamic Taint for Mobile Security Threats ...

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IEEE International Conference on Smart Cloud (IEEE SmartCloud)

作者： Yang, Tianda Qian, Kai Li, Lei Lo, Dan Tao, Lixin Kennesaw State Univ Coll Comp & Software Engn Marietta GA 30060 USA Pace Univ Dept Comp Sci Pleasantville NY USA

ISBN: (纸本)9781509052639

In the last decade, the computing landscape has been rapidly shifting to mobile platform. More and more individuals and businesses are using smartphones and tablet pcs as their main general purpose computing devices. As such, mobile platform becomes prominent targets of cybercriminals. It's imperative to develop techniques that can detect mobile malware and protect the mobile devices from cyber-attacks. In this paper, we propose a hybrid approach combing a static mining algorithm and a dynamic taint analysis for effective mobile malware analysis. A preliminary prototype was built and tested on real-world mobile apps.

关键词： Android mobile security analysis mining algorithm static analysis dynamic taint analysis

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mining Accompanying Passing-Vehicles to Discover Suspected Gangs 19

Mining Accompanying Passing-Vehicles to Discover Suspected G...

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19th IEEE International Symposium on High Assurance Systems Engineering (HASE)

作者： Wei Zhihao Li Jianyuan Li Wanqing Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Zhejiang Peoples R China Inst Enjoyor Co Ltd Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538685402

The accompanying vehicles usually provide valuable hints to discover the suspected gangs.. In this paper, using Hadoop, a new and efficient algorithm named TMFS is proposed for mining the spatial-and-temporal accompanying vehicles accurately and rapidly. Compared with the traditional algorithms, the veracity, run time and memory usage are all discussed in detail. Experimental results show that the Traffic MapReduced by Frequent Sequences (TMFS) algorithm is superior to Apriori algorithm and BIDE algorithm.

关键词： component Data mining Accompanying Passing-Vehicles Hadoop mining algorithm

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The Study and Application of E- business Website Based on the Web Data mining

The Study and Application of E- business Website Based on th...

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International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Wang, Yini Chongqing Business Vocat Coll Chongqing 400030 Peoples R China

Taking data mining technologies as the key, with the analysis of electronic commerce, this paper studies deeply on the implementation of data mining system that is electronic commerce-oriented.

ISBN: (纸本)9783037859391

Taking data mining technologies as the key, with the analysis of electronic commerce, this paper studies deeply on the implementation of data mining system that is electronic commerce-oriented.

关键词： Data mining Electronic Commerce mining algorithm

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Understanding Production Chain Business Process Using Process mining: A Case Study in the Manufacturing Scenario 28th

Understanding Production Chain Business Process Using Proces...

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28th International Conference on Advanced Information Systems Engineering (CAiSE)

作者： Bettacchi, Alessandro Polzonetti, Alberto Re, Barbara Univ Camerino Div Comp Sci Camerino Italy

ISBN: (纸本)9783319395647;9783319395630

Due to the continuous market change the enterprises need to react fast. To do that a better understanding of the way to work is needed. Indeed this was a real need of a manufacturing enterprise working in the production of coffee machines and selling them all over the world. In this paper, we present the experience made in the application of process mining techniques on a rich set of data that such enterprise collected during the last six years. We compare five mining algorithms, such as: alpha-algorithm, Heuristics Miner, Integer Linear Programming Miner, Inductive Miner, Evolutionary Tree Miner. We evaluated algorithms according to specific quality criteria: fitness, precision, generalization and simplicity. Even if comparison studies are already available in the literature we check them according to our working context. We conclude that the Inductive Miner algorithm is especially suited for discovering production chain processes in the context under study. The application of process mining gives the enterprise a comprehensive picture of the internal process organization. Resulting models were used by the company with successful results to motivate the discussion on the need of developing a flexible production chain.

关键词： Process mining Process discovery Business process ProM Framework mining algorithm Production chain

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