Text and Data Mining: The theory and practice of using TDM for scholarship in the humanities, Paul Verhaar

Автор: Paul Verhaar
Название: Text and Data Mining: The theory and practice of using TDM for scholarship in the humanities
ISBN: 1783304197 ISBN-13(EAN): 9781783304196
Издательство: Facet
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Цена: 91450.00 T
Наличие на складе: Нет в наличии.
Описание: This book offers a broad and accessible introduction to research based on text and data mining (TDM), focusing specifically on the ways in which TDM has been applied within the humanities.

Автор: Tsau Young Lin; Ying Xie; Anita Wasilewska; Churn-
Название: Data Mining: Foundations and Practice
ISBN: 3642097227 ISBN-13(EAN): 9783642097225
Издательство: Springer
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Цена: 278580.00 T
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Описание: This book contains valuable studies in data mining from both foundational and practical perspectives. It covers a broad range of subjects, from a conceptual framework of data mining to the role of sample size, as well as many different fields of data mining.

Автор: Linoff Gordon S.
Название: Data Analysis Using SQL and Excel
ISBN: 111902143X ISBN-13(EAN): 9781119021438
Издательство: Wiley
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Цена: 41190.00 T
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Описание: A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis SQL and Excel to perform sophisticated data analysis without the need for complex and expensive data mining tools.

Автор: Dong Guozhu
Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
ISBN: 1681735040 ISBN-13(EAN): 9781681735047
Издательство: Mare Nostrum (Eurospan)
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Цена: 77610.00 T
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Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

Автор: Pawel Cichosz
Название: Data Mining Algorithms: Explained Using R
ISBN: 111833258X ISBN-13(EAN): 9781118332580
Издательство: Wiley
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Цена: 66470.00 T
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Описание: Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles.

Автор: Kwartler
Название: Text Mining in Practice with R
ISBN: 1119282012 ISBN-13(EAN): 9781119282013
Издательство: Wiley
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Цена: 58030.00 T
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Описание: A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information.

Автор: Flanders, Julia
Название: The Shape of Data in Digital Humanities Modeling Texts and Text-based Resources
ISBN: 0367584034 ISBN-13(EAN): 9780367584030
Издательство: Taylor&Francis
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Цена: 43890.00 T
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Описание: This book addresses the need of humanities scholars for whom technology and data now play a large role in their research and teaching and who need deeper expertise in the area of data modeling and representation. The authors, all experts in digital humanities, offer a clear explanation of key technical principles, a grounded discussion of case stud

Автор: Jeffrey W. Alstete
Название: Evaluating Scholarship and Research Impact: History, Practices, and Policy Development
ISBN: 1787563901 ISBN-13(EAN): 9781787563902
Издательство: Emerald
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Цена: 66390.00 T
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Описание: This book probes the dynamics of academic research and scholarship evaluation. Readers will learn about scholarly metric evolution, impact factors, disruptive technologies, and a myriad of forces affecting policy development at institutions through an examination of widely-used measurements and growing concerns about their influence.

Автор: Matignon
Название: Data Mining Using SAS Enterprise Miner
ISBN: 0470149019 ISBN-13(EAN): 9780470149010
Издательство: Wiley
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Цена: 123500.00 T
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Описание: Data Mining Using SAS(r) Enterprise Miner introduces the reader to a wide variety of data mining techniques in SAS(r) Enterprise Miner. This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of Enterprise Miner with regard to SEMMA design and data mining analysis.

Автор: Klepac, Kopal & Mrsic
Название: Developing Churn Models Using Data Mining Techniques And Social Network Analysis
ISBN: 1466662883 ISBN-13(EAN): 9781466662889
Издательство: Mare Nostrum (Eurospan)
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Цена: 180180.00 T
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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.

Автор: Vandana Sakhre, Uday Pratap Singh
Название: Reactive Distillation: Advanced Control using Neural Networks
ISBN: 3110656140 ISBN-13(EAN): 9783110656145
Издательство: Walter de Gruyter
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Цена: 86720.00 T
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Описание:

Neural Networks is an integral part in machine learning and a known tool for controlling nonlinear processes. The area is under rapid development and provides a tool for modelling and controlling of advanced processes. This book provides a comprehensive overview for modelling, simulation, measurement and control strategies for reactive distillations using neural networks.

Автор: Bhatnagar Vishal
Название: Collaborative Filtering Using Data Mining and Analysis
ISBN: 1522504893 ISBN-13(EAN): 9781522504894
Издательство: Mare Nostrum (Eurospan)
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Цена: 189420.00 T
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Описание: Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.