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24th IEEE international Conference on data Engineering/ 1st international workshop on Secure Semantic Web

作者： Ferlez, Jure Faloutsos, Christos Leskovec, Jure Mladenic, Dunja Grobelnik, Marko Jozef Stefan Inst Dept Knowledge Technol Jamova 39 Ljubljana 1000 Slovenia Carnegie Mellon Univ Sch Comp Sci Machine Learning Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9781424418367

Given publication titles and authors, what can we say about the evolution of scientific topics and communities over time? Which communities shrunk, which emerged, and which split, over time? And, when in time were the turning points? We propose TimeFall, which can automatically answer these questions given a social network/graph that evolves over time. The main novelty of the proposed approach is that it needs no user-defined parameters, relying instead on the principle of Mnimum Description Length (MDL), to extract the communities, and to find good cut-points in time when communities change abruptly: a cut-point is good, if it leads to shorter data description. We illustrate our algorithm on synthetic and large real datasets, and we show that the results of the TimeFall agree with human intuition.

关键词： data description

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Advancing Knowledge Discovery and data mining

Advancing Knowledge Discovery and Data Mining

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1st international workshop on Knowledge Discovery and data mining (WKDD 2008)

作者： Qi Luo School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China Information Engineering School Wuhan University of Science and Technology Wuhan Hubei China

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and data mining, and Knowledge Discovery in database (KDD) process are presented in the paper. data mining theory, data mining tasks, data mining technology and data mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.

关键词： data mining Visual databases machine learning statistics Knowledge acquisition data visualization High performance computing Artificial intelligence Industrial relations mining industry

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Parallel and Sequential Optical Computing

Parallel and Sequential Optical Computing

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1st international workshop on Optical SuperComputing

作者： Woods, Darnien Naughton, Thomas J. Univ Seville Dept Comp Sci & Artificial Intelligence Seville Spain Natl Univ Ireland Dept Comp Sci Maynooth Kildare Ireland Univ Oulu Oulu Southern Inst RFMedia Lab Ylivieska 84100 Finland

ISBN: (纸本)9783540856726

We present a number of computational complexity results for an Optical model of computation called the continuous space machine. We also describe an implementation for an optical computing algorithm that, can be easily defined within the model. Our optical model is designed to model a wide class of optical computers, such as matrix vector multipliers and pattern recognition architectures. It is known that the model solves intractable PSPACE problems in polynomial time, and NC problems in polylogarithmic time. Both of these results use large spatial resolution (number of pixels). Here we look at what happens when we have constant spatial resolution. It turns out that we obtain similar results by exploiting other resources, such as dynamic range and amplitude resolution. However, with certain other restrictions we essentially have a sequential device. Thus we are exploring the border between parallel and sequential computation in optical computing. We describe an optical architecture for the unordered search problem of finding a one in a list of zeros. We argue that our algorithm scales well, and is relatively straightforward to implement. This problem is easily parallelisable and is from the class NC. We go on to argue that the optical computing community should focus their attention on problems within P (and especially NC), rather than developing systems for tackling intractable problems.

关键词： Optical data processing

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Towards privacy-preserving model selection

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2008年 4890 LNCS卷 138-152页

作者： Yang, Zhiqiang Zhong, Sheng Wright, Rebecca N. Department of Computer Science Stevens Institute of Technology Hoboken NJ 07030 United States Department of Computer Science SUNY Buffalo Buffalo NY 14260 United States Department of Computer Science DIMACS Rutgers University Piscataway NJ 08854 United States

ISBN: (纸本)3540784772

Model selection is an important problem in statistics, machine learning, and data mining. In this paper, we investigate the problem of enabling multiple parties to perform model selection on their distributed data in a privacy-preserving fashion without revealing their data to each other. We specifically study cross validation, a standard method of model selection, in the setting in which two parties hold a vertically partitioned database. For a specific kind of vertical partitioning, we show how the participants can carry out privacy-preserving cross validation in order to select among a number of candidate models without revealing their data to each other. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： data privacy

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Web usage mining to evaluate the transfer of learning in a web-based learning environment

Web usage mining to evaluate the transfer of learning in a w...

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1st international workshop on Knowledge Discovery and data mining

作者： Chanchary, Farah Habib Haque, Indrani Khalid, Md. Saifuddin Independent Univ Dhaka Bangladesh

ISBN: (纸本)9780769530901

Application of data mining for web log analysis has received significant attention in finding customers' behavioral pattern in e-commerce and learners' behavioral pattern in e-learning. While hit-counts indicate customers' interest in the product or purchasing behavior, a student's visits to a learning Management System (LMS) do not necessarily involve transfer of learning. Addressing such complexity in e-learning, this study analyzed students' log of a learning Management System (LMS) of two subjects at a university in Bangladesh, taught over six weeks duration. data mining and statistical tools have been used to rind relationships between students' LMS access behavior and overall performances. Results show that students having 'Low' access obtained poor grade, on campus access was higher than access from home. Background of students is very important for effective usage of web resources. Majority of the student considered LMS to be a quite helpful tool as teaching-learning method. Preparation and cleaning of the web-log files as well as application of data mining algorithms is important for learners' web usage analysis.

关键词： data mining e-learning

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Advancing knowledge discovery and data mining

Advancing knowledge discovery and data mining

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1st international workshop on Knowledge Discovery and data mining

作者： Luo, Qi Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430073 Peoples R China

ISBN: (纸本)9780769530901

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and data mining, and Knowledge Discovery in database (K-DD) process are presented in the paper. data mining theory, data mining tasks, data mining technology and data mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.

关键词： data mining

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Influences of CAI English teaching pattern on the autonomous learning - Research on divided class instruction of independent college

Influences of CAI English teaching pattern on the autonomous...

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1st international workshop on Knowledge Discovery and data mining

作者： Tian, Xianzhi Wuhan Univ Sci & Technol Dept English Fac English Educ ZhongNan Branch Wuhan 430223 Peoples R China

ISBN: (纸本)9780769530901

With the development of the technology of C,41, it is widely used in our English teaching. So many people focus their attentions on the influences of CAI in different aspects. Therefore, the author tries to investigate the influences of CAI English teaching pattern on the Autonomous learning. in divided class instruction of independent college. As far as it is concerned, it is important for us to find out the essential influences of" divided class instruction" on students in independent college, thus to make the reform of our teaching pattern successfully.

关键词： students

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Research of a virtual 3D study pattern based on constructive theory in e-learning

Research of a virtual 3D study pattern based on constructive...

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1st international workshop on Knowledge Discovery and data mining

作者： Duan, Xinyu Jiang, Ping Anyang Normal Univ Med Res Inst Anyang Henan Province Peoples R China Anyang 20th Middle Sch Exp Sec Anyang Peoples R China

ISBN: (纸本)9780769530901

E-learning is most important by its role in distance teaching, and as supplementary learning material, it's mainly used in higher education area. Paper first introduced the significance of e-learning and application of constructive theory. In contrast with traditional education, its application possesses broad prospects. Then we made a systematic exposition of interrelated theory basis, discussed a new 3D virtual study pattern. Lastly, it presented two application systems that we developed One is a virtual audio-video multimedia teaching center which has good immersion feelings;another is a virtual music appreciation environment which has dynamic interaction performance.

关键词： e-learning virtual study constructive theory entity

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Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

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international JOURNAL OF APPROXIMATE REASONING 2007年第1期44卷 4-31页

作者： Ishibuchi, Hisao Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the in terpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretabitity-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns. (C) 2006 Elsevier Inc. All rights reserved.

关键词： classification fuzzy systems fuzzy data mining multiobjective optimization genetic algorithms genetics-based machine learning

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A novel rule ordering approach in classification association rule mining 1

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5th international Conference on machine learning and data mining in pattern recognition

作者： Wang, Yanbo J. Xin, Qin Coenen, Frans Univ Liverpool Dept Comp Sci Ashton Bldg Ashton St Liverpool L69 3BX Merseyside England Univ Bergen Dept Informat N-5020 Bergen Norway

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

A Classification Association Rule (CAR), a common type of mined knowledge in data mining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an "antecedent double right arrow consequent-class" rule. Classification Association Rule mining (CARM) is a recent Classification Rule mining (CRM) approach that builds an Association Rule mining (ARM) based classifier using CARs. Regardless of which particular methodology is used to build it, a classifier is usually presented as an ordered CAR list, based on an applied rule ordering strategy. Five existing rule ordering mechanisms can be identified: (1) Confidence-Support-size -of-Antecedent (CSA), (2) size-of-Antecedent-Confidence-Support (ACS), (3) Weighted Relative Accuracy (WRA), (4) Laplace Accuracy, and (5) chi(2) Testing. In this paper, we divide the above mechanisms into two groups: (i) pure "support-confidence" framework like, and (ii) additive score assigning like. We consequently propose a hybrid rule ordering approach by combining one approach taken from (i) and another approach taken from (ii). The experimental results show that the proposed rule ordering approach performs well with respect to the accuracy of classification.

关键词： classification association rules classification association rule mining data mining rule ordering

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