Angebote zu "Mining" (123 Treffer)

Data Mining
39,99 € *
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today´s techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book

Anbieter: buecher.de
Stand: 13.10.2017
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Data Mining
39,95 € *
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In den modernen Datenbanken steckt unentdecktes Wissen, das ohne geeignete Hilfsmittel kaum gefördert werden kann. Hier setzt das Data Mining an und liefert Methoden und Algorithmen, um bisher unbekannte Zusammenhänge zu entdecken. Das Buch deckt den Stoff einer einsemestrigen Vorlesung an Universitäten oder Fachhochschulen ab und ist als klassisches Lehrbuch konzipiert. Es bietet Zusammenfassungen, zahlreiche Beispiele und Übungsaufgaben. Jürgen Cleve, Uwe Lämmel ; Hochschule Wismar.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Data Mining
39,95 € *
ggf. zzgl. Versand

In den modernen Datenbanken steckt unentdecktes Wissen, das ohne geeignete Hilfsmittel kaum gefördert werden kann. Hier setzt das Data Mining an und liefert Methoden und Algorithmen, um bisher unbekannte Zusammenhänge zu entdecken. Das Buch deckt den Stoff einer einsemestrigen Vorlesung an Universitäten oder Fachhochschulen ab und ist als klassisches Lehrbuch konzipiert. Es bietet Zusammenfassungen, zahlreiche Beispiele und Übungsaufgaben. Jürgen Cleve, Uwe Lämmel ; Hochschule Wismar.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Mining Unlabeled Event Log
32,90 € *
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Mining Unlabeled Event Log

Anbieter: Allyouneed.com
Stand: 17.10.2017
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Data Mining and Predictive Analytics
120,99 € *
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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition : Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. Daniel T. Larose is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. He has published several books, including Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Wiley, 2007) and Discovering Knowledge in Data: An Introduction to Data Mining (Wiley, 2005). In addition to his scholarly work, Dr. Larose is a consultant in data mining and statistical analysis working with many high profile clients, including Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc. Chantal D. Larose is a Ph.D. candidate in Statistics at the University of Connecticut. Her research focuses on the imputation of missing data and model-based clustering. She has taught undergraduate statistics since 2011, and is a statistical consultant for DataMiningConsultant.com, LLC.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Data Mining and Learning Analytics - Applicatio...
112,99 € *
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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data minings four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDMs emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research. Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education. Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. He has authored several books and has contributed chapters and articles in more than 60 publications. Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010. Osmar R. Zaïane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Data Mining and Learning Analytics - Applicatio...
112,99 € *
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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data minings four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDMs emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research. Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education. Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. He has authored several books and has contributed chapters and articles in more than 60 publications. Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010. Osmar R. Zaïane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Data Mining for Social Robotics - Toward Autono...
101,14 € *
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This book explores an approach to social robotics based solely on autonomous unsupervised techniques and positions it within a structured exposition of related research in psychology, neuroscience, HRI, and data mining. The authors present an autonomous and developmental approach that allows the robot to learn interactive behavior by imitating humans using algorithms from time-series analysis and machine learning. The first part provides a comprehensive and structured introduction to time-series analysis, change point discovery, motif discovery and causality analysis focusing on possible applicability to HRI problems. Detailed explanations of all the algorithms involved are provided with open-source implementations in MATLAB enabling the reader to experiment with them. Imitation and simulation are the key technologies used to attain social behavior autonomously in the proposed approach. Part two gives the reader a wide overview of research in these areas in psychology, and ethology. Based on this background, the authors discuss approaches to endow robots with the ability to autonomously learn how to be social. Data Mining for Social Robots will be essential reading for graduate students and practitioners interested in social and developmental robotics.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Using Subsequence Mining to Identify Business P...
29,99 € *
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Masters Thesis from the year 2016 in the subject Computer Science - Commercial Information Technology, grade: -, Hamburg University of Technology (TUHH; Universität zu Lübeck), language: English, abstract: To manage business processes, companies must previously define, configure, implement and enact them. Analysts try to identify companies business processes. However, large companies might have complex business processs (BPs) and consist of many business units. Therefore, classical business process modelling hardly scales. Both, companies and analysts are interested in automated approaches for business process modelling, saving time and money. Todays business process analysts often use process mining techniques to extract companys business processes by analyzing event logs of applications. This technique has its limitations, and is strongly dependent on the kind of log files of deployed applications. By designing our mission oriented network analysis (MONA) approach using algorithms having polynomial complexity, we show that identification of business processes is tractable. Identification of related tasks which constitute business processes is based on analysis of communication patterns in network traffic. We assume that todays business processes are based on network-aided applications. Our software presents identified business processes using business process modelling notation.

Anbieter: ciando eBooks
Stand: 11.07.2017
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Fraud Detection. Data-Mining-Verfahren zur Aufd...
29,99 € *
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Masterarbeit aus dem Jahr 2015 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,0, Europäische Fachhochschule Brühl (Wirtschaftsinformatik), Sprache: Deutsch, Abstract: Die Ausgaben für das Gesundheitswesen beliefen sich in Deutschland im Jahr 2014 auf circa 314,9 Milliarden Euro. Es wird davon ausgegangen, dass etwa fünf bis sieben Prozent der Gesamtkosten durch Betrug bei der Abrechnung entstehen. Die Konvergenz von neuen Kommunikationstechnologien und Innovationen aus dem Bereich der Telemedizin stimuliert in diesen Zusammenhang den Trend zu Big Data. Insbesondere das Datenvolumen im Gesundheitswesen ist hiervon betroffen und es wird zunehmend schwieriger, wertvolle Informationen zur Aufdeckung von Abrechnungsbetrug aus der Datenflut zu extrahieren. Die Anwendungsmöglichkeiten von Fraud im Gesundheitswesen sind vielfältig und umfassen neben einer Abrechnungsfälschung ebenfalls Rezeptfälschung und Chipkartenmissbrauch. Nach Ansicht des Bundeskriminalamtes handelt es sich bei Abrechnungsfälschung um eine besonders sozialschädliche Form der Wirtschaftskriminalität, da die Integrität des Gesundheitswesens negativ beeinflusst wird. Die Mehrausgaben führen dabei zu steigenden Beiträgen für Krankenversicherungen. Ein Fehlverhalten ist bei allen Gruppen der Leistungserbringer im Gesundheitswesen aufzufinden. Die Ärzte gehörten mit einem Anteil von 14,6 Prozent zur Leistungserbringergruppe, die am häufigsten unter Tatverdacht steht. Jede Krankenkasse ist gesetzlich verpflichtet, eine Stelle zur Bekämpfung von Fehlverhalten im Gesundheitswesen einzurichten. Darüber hinaus kann ein Krankenversicherungsunternehmen mithilfe einer erfolgreichen Schadensbekämpfung einen Wettbewerbsvorteil erlangen. Für die Abrechnung der deutschen Hausärzte, gegenüber den gesetzlichen Krankenkassen, ist die Kassenärztliche Vereinigung (KV) für die Versicherte im Kollektivvertrag und der Deutschen Hausärzteverband e. V. für Versicherte im Selektivvertrag zuständig. Jedes dieser Unternehmen verfügt über ein Rechenzentrum zur Verarbeitung der Abrechnungsdaten. Die Abrechnungsdaten werden von den Arztinformationssystemen (AIS) entgegengenommen und den Krankenkassen gegenüber in Rechnung gestellt. Hauptaufgabe der Rechenzentren ist es eine korrekte Abrechnung gegenüber den Krankenkassen zu erstellen. Ein System zur Aufdeckung von FraudÄrzten auf Basis von nicht-trivialen Informationen wurde bisher nicht entwickelt und könnte dazu beitragen, dass die Kosten im Gesundheitswesen gesenkt werden. Ziel ist es, zu prüfen, wie Data-Mining-Verfahren zur Aufdeckung von Abrechnungsbetrug genutzt werden können. [...]

Anbieter: ciando eBooks
Stand: 11.07.2017
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