Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Автор: Ledolter Johannes Название: Data Mining and Business Analytics with R ISBN: 111844714X ISBN-13(EAN): 9781118447147 Издательство: Wiley Рейтинг: Цена: 17258.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets.
Автор: Larose Daniel T Название: Data Mining and Predictive Analytics ISBN: 1118116194 ISBN-13(EAN): 9781118116197 Издательство: Wiley Рейтинг: Цена: 18842.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery.
Автор: Hamstra Mark, Zaharia Matei Название: Learning Spark: Lightning-Fast Big Data Analytics ISBN: 1449358624 ISBN-13(EAN): 9781449358624 Издательство: Wiley Рейтинг: Цена: 5067.00 р. Наличие на складе: Поставка под заказ.
Описание: Written by the developers of Spark, this book will have data scientists and engineers up and running in no time.
Автор: Jiawei Han Название: Data Mining: Concepts and Techniques, ISBN: 0123814790 ISBN-13(EAN): 9780123814791 Издательство: Elsevier Science Рейтинг: Цена: 9720.00 р. Наличие на складе: Нет в наличии.
Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Описание: Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and manageing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios.Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.
Описание: Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution.
Автор: ElAtia Samira Название: Handbook of Data Mining and Learning Analytics ISBN: 1118998235 ISBN-13(EAN): 9781118998236 Издательство: Wiley Рейтинг: Цена: 17258.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Xu & Li Название: Social Media Mining And Social Network Analysis ISBN: 1466628065 ISBN-13(EAN): 9781466628069 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 25502.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The widespread use of web-based communities, social media, and social networking sites has brought a rapid change to the interaction between computers and users as well as the digital experience as a whole. <br><br><em>Social Media Mining and Social Network Analysis: Emerging Research</em> highlights the advancements made in social network analysis and social web mining and its influence in the fields of computer science, information systems, sociology, organisation science discipline and much more. This collection of perspectives on developmental practice is useful for industrial practitioners as well as researchers and scholars.
Автор: Bhatnagar Название: Data Mining In Dynamic Social Networks And Fuzzy Systems ISBN: 1466642130 ISBN-13(EAN): 9781466642133 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: <em>Data Mining in Dynamic Social Networks and Fuzzy Systems</em> brings together research on the latest trends and patterns of data mining tools and techniques in dynamic social networks and fuzzy systems. With these improved modern techniques of data mining, this publication aims to provide insight and support to researchers and professionals concerned with the management of expertise, knowledge, information, and organisational development.
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