探索数据挖掘分类技术在高校教学中的应用

数据挖掘是致力于数据分析和理解、揭示数据内部蕴藏知识的技术。由于数据库中存在着大量数据,因此从数据库中发现有用的信息显得十分重要。对数据挖掘技术的研究,国内外己经取得了许多令人瞩日的成就,并成功地应用到了许多领域,但在教育领域中的应用并不广泛。探索在高校教学中数据挖掘分类技术的应用,提出数据挖掘技术在高校教学应用中的实施方案,并以高校教学中学生成绩的分析为例介绍方案的实施过程。     

高校教育信息化中数据挖掘的应用研究

数据挖掘作为一种高效的数据处理技术,被广泛的应用到各个领域。高校教育信息化的快速发展为高校积累了大量的信息,利用数据挖掘技术可以发现在数据中隐藏的普遍规律和模式,为高校教育教学工作的顺利开展提供了科学的依据。本文通过对数据挖掘概念、过程及进行数据挖掘必要性的分析,探讨数据挖掘技术在高校教育信息化中的应用问题。     

数据挖掘技术在高校教学管理中的应用

作为教育信息化的重要组成部分,高校教学管理系统中收集了大量的教学信息,但大多没有得到很好的挖掘和研究,所以数据挖掘在高校教学管理系统中的应用具有现实意义。该文介绍了数据挖掘技术的基本原理和解决问题的方法,并讨论了一种将数据挖掘技术与高校教学管理系统相结合的方法,提高了高校教学管理的工作效率,实现了教学资源安排的合理性,在高校教学信息化建设方面做出了新的探索。     

基于大数据挖掘分析的高校信息化建设探索

高校信息化建设过程中积累了大量的数据资源,如何利用数据挖掘技术获取有价值的信息是高校大数据分析中的一个重要问题。介绍了几种典型的数据挖掘技术,并且通过分析其在学生信息管理中的应用,说明了数据挖掘是一种在高校信息化建设中提高学校的管理能力和科研教学水平的有效技术。     

数据挖掘技术在高校管理中的应用

本文介绍了数据挖掘的概念、几种知识模型和主要技术方法，分析数据挖掘技术在高校教育教学中的应用和存在问题。     

决策树技术在高师生教学技能考核评价系统中的应用研究

目前,高校在学籍管理、成绩管理、师资管理等方面积累了大量的数据资源,如何发挥数据挖掘技术的优势,将数据挖掘技术应用于高校的教学管理中,成为了当前国内外关注的学术热点和前沿性课题。本文主要是对数据挖掘技术在高师生教学技能考核评价中的应用进行研究。     

数据挖掘在高校教学及学生学习评价中的应用空间分析

数据挖掘技术目前在商业、金融业等方面都得到了广泛的应用,而在教育领域应用较少,本文通过对数据挖掘在高校信息化中的应用空间分析,提供了一个学习者特征分析模式,帮助教学人员合理安排教学工作,协助辅导员对学生的管理,对提高学校的教学管理水平起到指导作用,为数据挖掘技术有效应用于教育领域研究提供了一个方向。     

基于数据挖掘技术的高校教育管理研究

教育信息化的不断普及和发展,使得高校数据库中保存了越来越多的教育数据,这对高校的教育管理提出了新的挑战。数据挖掘技术的广泛应用为高校教育管理信息化提供了技术支持。基于此,主要研究了数据挖掘技术在教学、学生管理、就业等方面的应用,以推动高校教育事业的发展。     

数据挖掘在高校教学及学生学习评价中的应用空间分析

数据挖掘技术目前在商业、金融业等方面都得到了广泛的应用，而在教育领域应用较少，本文通过对数据挖掘在高校信息化中的应用空间分析．提供了一个学习者特征分析模式，帮助教学人员合理安排教学工作，协助辅导员对学生的管理，对提高学校的教学管理水平起到指导作用．为数据挖掘技术有效应用于教育领域研究提供了一个方向。     

数据挖掘在设备管理中的应用探究

数据挖掘技术有其自身的优势,能够有效解决传统数据分析方法所无法解决的难题,从而提高处理数据的能力。近年来,数据挖掘技术在我国得到了广泛的发展,将其应用到高校设备的管理工作中,对于提高高校设备的管理水平,具有重要的意义。该文分析了高校设备管理中存在的问题和数据挖掘技术,探究了将数据挖掘技术应用到高校管理工作中后,高校设备管理水平上升的具体表现。     

数据挖掘技术在计算机基础教学模式改革中的应用

《大学计算机基础》是非计算机专业学生掌握计算机技术的入门课程,为提高该课程的教学质量和教学效果,更好地培养出具有计算机应用能力的专业人才,采用数据挖掘技术对计算机基础教学方面的调查问卷进行挖掘,为学校计算机基础课程实施"任务-探究式"教学模式提供了决策支持。教学实践表明,该教学模式符合计算机基础课程特点,提高了非计算机专业学生利用计算机解决问题的能力。     

Web挖掘在教学平台中的应用研究

介绍Web数据挖掘的基本概念、主要过程、方法,并利用Web数据挖掘对教学平台中的网络日志进行具体分析,探讨Web数据挖掘在应用过程中存在的一些问题。     

Efficient Incremental Maintenance of Frequent Patterns with FP-Tree

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 new 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).     

基于数据挖掘的高校网络教学平台构建与研究

本文分析了高校网络教学平台的现状,所面临着无法科学的得到学生利用网络教学平台学习的反馈,从而引出了数据挖掘技术。介绍了数据挖掘技术的基本情况,阐述了数据挖掘技术在网络教学平台中所起到的作用,设计了基于数据挖掘技术的网络教学平台,并对本项研究进行了总结和展望。     

分类挖掘在中职教学中的应用

通过分类挖掘技术促进中职教学的发展。首先讨论了中职教学的现状及其特点,然后介绍了分类挖掘的概念及相关技术。以及描述了决策树分类挖掘算法。并通过实例说明了怎样利用分类挖掘工具进行信息挖掘。最后运用分类挖掘技术找出数据库中有效信息,帮助教师全面了解学生,从而针对学生的各项特征作出教学策略的调整,以达到提高教学水平的目的。     

基于数据挖掘的AHP算法在职业教育教学评价系统中的应用

以当前的职业学校教育教学为平台,就如何将数据挖掘与教学质量评价指标相结合的问题进行研究,通过借鉴国内外职业教师教学质量评价指标体系的有效挖掘,解决目前教学质量评价指标分数比例的不合理性,提出运用AHP算法来确定教学质量评价指标比例,使教学质量评价公平、公正、合理、高效。     

Exploiting edge semantics in citation graphs using efficient, vertical ARM

Graphs are increasingly becoming a vital source of information within which a great deal of semantics is embedded. As the size of available graphs increases, our ability to arrive at the embedded semantics grows into a much more complicated task. One form of important hidden semantics is that which is embedded in the edges of directed graphs. Citation graphs serve as a good example in this context. This paper attempts to understand temporal aspects in publication trends through citation graphs, by identifying patterns in the subject matters of scientific publications using an efficient, vertical association rule mining model. Such patterns can (a) indicate subject-matter evolutionary history, (b) highlight subject-matter future extensions, and (c) give insights on the potential effects of current research on future research. We highlight our major differences with previous work in the areas of graph mining, citation mining, and Web-structure mining, propose an efficient vertical data representation model, introduce a new subjective interestingness measure for evaluating patterns with a special focus on those patterns that signify strong associations between properties of cited papers and citing papers, and present an efficient algorithm for the purpose of discovering rules of interest followed by a detailed experimental analysis. Imad Rahal is a newly appointed assistant professor in the Department of Computer Science at the College of Saint Benedict ∣ Saint John's University, Collegeville, MN, and a Ph.D. candidate at North Dakota State University, Fargo, ND. In August 2003, he earned his master's degree in computer science from North Dakota State University. Prior to that, he graduated summa cum laude from the Lebanese American University, Beirut, Lebanon, in February 2001 with a bachelor's degree in computer science. Currently, he is completing the final requirements for his Ph.D. degree in computer science on an NSF ND-EPSCoR doctoral dissertation assistantship with August of 2005 as a projected completion date. He is very active in research, proposal writing, and publications; his research interests are largely in the broad areas of data mining, machine learning, databases, artificial intelligence, and bioinformatics. Dongmei Ren is working for the Database Technology Institute for z/OS, IBM Silicon Valley Lab, San Jose, CA, as a staff software engineer. She holds a Ph.D. degree from North Dakota State University, Fargo, ND, and master's and bachelor's degrees from TianJin University, TianJin, China. She has been a software engineer at DaTang Telecommunications, Beijing, China. Her areas of expertise are outlier analysis, data mining and knowledge discovery, database systems, machine learning, intelligent systems, wireless networks and bioinformatics. She has been awarded the Siemens Scholarship research enhancement for excellent performance in study and research. She is a member of ACM, IEEE. Weihua Wu is a network monitoring & managed services analyst at Hewlett-Packard Co. in Canada. He holds a master's degree from North Dakota State University and a bachelor's degree from Nanjing University, both in computer science. His research areas of interest include data mining, knowledge discovery, data warehousing, information technology, network security, and bioinformatics. He has participated in various projects supported by NSF, DARPA, NASA, USDA, and GSA grants. Anne Denton is an assistant professor in computer science at North Dakota State University. Her research interests are in data mining, knowledge discovery in scientific data, and bioinformatics. Specific interests include data mining of diverse data, in which objects are characterized by a variety of properties such as numerical and categorical attributes, graphs, sequences, time-dependent attributes, and others. She received her Ph.D. in physics from the University of Mainz, Germany, and her M.S. in computer science from North Dakota State University, Fargo, ND. Christopher Besemann received his M.Sc. in computer science from North Dakota State University in Fargo, ND, 2005. Currently, he works in data mining research topics including association mining and relational data mining with recent work in model integration as a research assistant. He is accepted under a fellowship program for Ph.D. study at North Dakota State University. William Perrizo is a professor of computer science at North Dakota State University. He holds a Ph.D. degree from the University of Minnesota, a master's degree from the University of Wisconsin and a bachelor's degree from St. John's University. He has been a research scientist at the IBM Advanced Business Systems Division and the U.S. Air Force Electronic Systems Division. His areas of expertise are data mining, knowledge discovery, database systems, distributed database systems, high speed computer and communications networks, precision agriculture and bioinformatics. He is a member of ISCA, ACM, IEEE, IAAA, and AAAS.     

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work. Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing. Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.     

数据挖掘技术在学校管理中的应用

数据挖掘技术是一门多学科相互交叉融合而形成的新兴学科。目前,该技术已在商业、金融业、农业、互联网、医药业等多个领域中得到广泛应用。而将数据挖掘技术与学校管理相结合,可以从大量事务管理数据中提取出了隐藏在其中的有用信息,因此可以帮助教学人员合理安排教学工作,协助辅导员对学生的管理,从而促进教育体制的进一步完善与发展。本文由数据挖掘技术概述入手,论述了数据挖掘技术在学校管理中的作用,最后,将数据挖掘技术应用在学生成绩管理中,可以实现透过现象看本质,提炼有价值的信息。     

Web数据挖掘技术及工具研究

Internet应用的普及使得数据挖掘技术的重点已经从传统的基于数据库的应用转移到了基于Web的应用。文章就Web挖掘技术的概念、分类及文本挖掘和用户访问模式挖掘的实现技术做了详细的阐述,并在此基础上介绍了一些实用的Web挖掘工具。