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IEEE International Conference on data Mining (ICDM)

作者： Robert Jaschke Andreas Hotho Christoph Schmitz Bernhard Ganter Gerd Stumme Knowledge & Data Engineering Group University of Kassel Germany Institute for Algebra Dresden University of Technology Germany

In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed item-sets to three-dimensional data to allow for mining folk-sonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.

关键词： data mining Itemsets Resource management Algorithm design and analysis Association rules data structures data engineering Algebra Lattices Clustering algorithms

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Mining Maximal Frequent Patterns in a Unidirectional FP-tree

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Journal of Donghua University(English Edition) 2006年第6期000卷 105-109页

作者： SONG Jing-jing LIU Rui-xin WANG Yan JIANG Bao-qing SONG Jing-jing(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001) LIU Rui-xin(Information Engineering Science Department Yellow River Conservancy Technical Institute Kaifeng 475003) WANG Yan(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management Zhengzhou 450015)

Becausemining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However,because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques:single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_ FP-Max further lowers the expense of time and space.

关键词： data mining frequent pattern the maximal frequent pattern Unid _ FP-tree conditional Unid _ FP-tree

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Algebraic Independence Measures for the Values of Mahler Type Functions

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Chinese Quarterly Journal of Mathematics 2006年第4期21卷 567-576页

作者： WANG Tian-qin LIU Hua-ke Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 China College of Mathematics and Information Science Henan University Kaifeng 475001 China

In this paper, we give the algebraic independence measures for the values ofMahler type functions in complex number field and p-adic number field, respectively.

关键词： algebraic independence measure Mahler type function p-adic number field

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Recent trends in data Mining (DM): Document Clustering of DM Publications

Recent trends in Data Mining (DM): Document Clustering of DM...

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International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Zhengxin Chen Yong Shi Peter Kiewit Institute ofInformation Science Technology & Engineering University of Nebraska Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

data mining (DM) brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. A large amount of data mining articles have been published. The goal of this study is to establish an overview of the past and current data mining research activities from the title and abstract more than 1400 textual documents collected from premier data mining journals and conference proceedings. Specifically, this study applied document clustering approaches to determine which subjects had been studied over the last several years, which subjects are currently popular, and describe the longitudinal changes of data mining publications

关键词： Delta modulation data mining Machine learning Conference proceedings IEL data engineering Visual databases Statistics data visualization Software libraries

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Application of Clustering Methods to Health Insurance Fraud Detection

Application of Clustering Methods to Health Insurance Fraud ...

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International Conference on Services Systems and Services Management, ICSSSM

作者： Yi Peng Gang Kou Alan Sabatka Zhengxin Chen Deepak Khazanchi Yong Shi Peter Kiewit Institute of Information Science Technology & Engineering University of Nebraska Omaha Omaha NE USA Mutual of Omaha Omaha NE USA Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Chinese Academy of Sciences Beijing China

Health insurance fraud detection is an important and challenging task. Traditionally, insurance companies use human inspections and heuristic rules to detect fraud. As the size of databases increases, the traditional approaches may miss a great portion of fraud for two main reasons. First, it is impossible to detect all health care fraud by manual inspection over large databases. Second, new types of health care fraud emerge constantly. SQL operations based on heuristic rules cannot identify those new emerging fraud schemes. Such a situation demands more sophisticated analytical methods and techniques that are capable of detecting fraud activities from large databases. The goal of this paper is to understand and detect suspicious health care frauds from large databases using clustering technique. Specifically, this paper applies two clustering methods, SAS EM and CLUTO, to a large real-life health insurance dataset and compares the performances of these two methods

关键词： Clustering methods Insurance Medical services Inspection Synthetic aperture sonar Humans data mining Spatial databases Cities and towns Input variables

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Semantics, Web and Mining: Preface

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) 2006年 4289 LNAI卷 v-vi页

作者： Berendt, Bettina Hotho, Andreas Mladenič, Dunja Semeraro, Giovanni Spiliopoulou, Myra Stumme, Gerd Van Someren, Maarten Ackermann, Markus Grobelnik, Marko Svátek, Vojtěch Institute of Information Systems Humboldt University Berlin Germany Knowledge and Data Engineering Group University of Kassel Germany J. Stefan Institute Ljubljana Slovenia Department of Informatics University of Bari Italy Faculty of Computer Science Otto-von-Guericke-Univ. Magdeburg Germany Informatics Institute University of Amsterdam Netherlands Dept. of Natural Language Processing Institute for Computer Science University of Leipzig Germany University of Economics Prague Czech Republic

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A Depth-first Algorithm of Finding All Association Rules Generated by a Frequent Itemset

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Journal of Donghua University(English Edition) 2006年第6期000卷

作者： WU Kun JIANG Bao-qing WEI Qing WU Kun(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 The Personnel Department Zhengzhou Institute of Aeronautical Industry ManagementZhengzhou 450000) JIANG Bao-qing(Institute of Data and Knowledge Engineering Henan University Kaifeng 475001) WEI Qing(The Computer Science Department Henan University of Finance and Economics Zhengzhou 450000)

The classical algorithm of finding association rules generated by a frequent itemset has to generate all nonempty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the *** show GRSET algorithm to be practicable and efficient.

关键词： association rule frequent itemset breath-first depth-first consequent

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Discrepancies between reality and expectation: Can Adaptive Hypermedia meet the expectations of teachers?

Discrepancies between reality and expectation: Can Adaptive ...

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IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2005

作者： Monthienvichienchai, Rachada Conlan, Owen Seyedarabi, Faezeh London Knowledge Lab. Institute of Education University of London United Kingdom Knowledge and Data Engineering Group Department of Computer Science Trinity College Dublin Ireland

ISBN: (纸本)9781627483308

The effort to personalise eLearning has led to the development of sophisticated Adaptive Hypermedia (AH) systems that can adapt to many aspects of the learner's characteristics or explicit preferences. However, while many of these adaptations are grounded in sound pedagogical theories, it is unclear how effective such adaptations are in supporting everyday teaching and learning processes that take place in the classroom today. This paper presents a study that explores what teachers in the UK, who have been named by their colleagues as implementing best-practice in the classroom, expect from an eLearning platform that can adapt to the individual needs of their students. The result of the study shows that there are currently some discrepancies between the teachers' expectations and what adaptive hypermedia currently offers. The paper then concludes by describing how these discrepancies can be addressed in future AH systems.

关键词： Students

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Can museum exhibits support personalised learning in collaborative classroom activities by using augmented reality?

Can museum exhibits support personalised learning in collabo...

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IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2005

ISBN: (纸本)9781627483308

This paper proposes a novel approach in integrating seemingly contradictory approaches of teaching and learning via collaborative and personalised learning activities by using augmented reality museum exhibits. The paper highlights the benefits that computer supported collaborative learning and computer supported personalised learning bring or promise to bring to the learner. A proposal is given for a study that will setup a common ground for these two approaches via a location-aware augmented reality museum display. The principal aim of the study is to investigate how the process of learning collaboratively is intertwined with personalised experience of visiting the museum display. Ultimately, the result of the study will provide a new understanding of visitor's experience of museum displays, not only within the context of the museum's location, but also within the classroom where the visit truly begins and finishes.

关键词： Augmented reality

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Prediction of wastewater pre-precipitation variables using self-organizing networks

Prediction of wastewater pre-precipitation variables using s...

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20th IEEE International Symposium on Intelligent Control, ISIC '05 and the13th Mediterranean Conference on Control and Automation, MED '05

作者： Isaias, Padelis Nilsson, Sara Stathaki, Anna King, Robert E. IEEE Data and Knowledge Engineering Group Computer Research Institute Athens Greece Environmental Research Institute IVL Stockholm Sweden Electrical Engineering and Computer Science University of Patras Greece Computer Research Institute Athens Greece

This paper describes the derivation and design of an array of self-organizing networks trained by inductive learning for one step ahead prediction of the outputs of the pre-precipitation stage of a wastewater treatmen... 详细信息

ISBN: (纸本)0780389360

关键词： Wastewater

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