Angebote zu "Mining" (179 Treffer)

Data Mining
39,80 € *
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The mountains of data in modern databases conceal undiscovered knowledge that can only be brought to light with the right tools. Data Mining seeks to provide methods and algorithms to uncover previously unknown connections. The book covers the material in a one-semester university or technical college course on data mining and is conceived as a classical textbook. It includes summaries, numerous examples, and practice exercises. Prof. Dr. Jürgen Cleve , Hochschule Wismar Prof. Dr. Uwe Lämmel , Hochschule Wismar

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Stand: 11.07.2017
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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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Data Mining For Dummies
23,99 € *
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Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your businesss entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesnt take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their businesss needs. In this book, youll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. Youll gain the confidence you need to start making data mining practices a routine part of your successful business. If youre serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.

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Stand: 11.07.2017
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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.

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Stand: 11.07.2017
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A Traffic Simulation Tool for Data Mining Analysis
64,90 € *
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A Traffic Simulation Tool for Data Mining Analysis

Anbieter: Allyouneed.com
Stand: 17.08.2017
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Integrarea tehnicilor Data Mining in platforme ...
55,90 € *
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Integrarea tehnicilor Data Mining in platforme de comer? electronic

Anbieter: Allyouneed.com
Stand: 17.08.2017
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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 € *
ggf. zzgl. Versand

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.

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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.

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Stand: 11.07.2017
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