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Descriptive Data Mining (Computational Risk Management)
Book 8 of 10: Computational Risk ManagementSeller: Books From California, Simi Valley, CA, U.S.A.
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hardcover. Condition: Very Good. Cover and edges may have some wear.
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Hardcover. Condition: Very Good. 2. Auflage. Unread, cover with shelfwear or minor damages. Immediately dispatched from Germany.
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Hardcover. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Paperback. Condition: new. Paperback. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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US$ 117.86
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Add to basketPaperback. Condition: Brand New. 144 pages. 9.13x6.14x0.24 inches. In Stock.
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Descriptive Data Mining (Computational Risk Management)
Book 7 of 10: Computational Risk ManagementSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Descriptive Data Mining (Computational Risk Management)
Book 8 of 10: Computational Risk ManagementSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Add to basketCondition: New. In English.
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Condition: New. pp. 116.
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Descriptive Data Mining
Language: English
Published by Springer Nature Customer Service Center Gmbh Jun 2019, 2019
ISBN 10: 9811371830 ISBN 13: 9789811371837
Taschenbuch. Condition: Neu. Neuware.
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Taschenbuch. Condition: Neu. Descriptive Data Mining | David L Olson | Taschenbuch | xi | Englisch | 2018 | Springer Singapore | EAN 9789811098475 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Descriptive Data Mining
Book 7 of 10: Computational Risk ManagementLanguage: English
Published by Springer Nature Singapore, 2018
ISBN 10: 9811098476 ISBN 13: 9789811098475
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers an overview of knowledge management. It starts with an introduction to the subject, placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis. Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Both R and Rattle are free to students. Chapter 3 then describes market basket analysis, comparing it with more advanced models, and addresses the concept of lift. Subsequently, Chapter 4 describes smarketing RFM models and compares it with more advanced predictive models. Next, Chapter 5 describes association rules, including the APriori algorithm and provides software support from R. Chapter 6 covers cluster analysis, including software support from R (Rattle), KNIME, and WEKA, all of which are open source. Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output. Chapter 8 concludes the monograph.Using business-related data to demonstrate models, this descriptive book explains how methods work with some citations, but without detailed references. The data sets and software selected are widely available and can easily be accessed.
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Descriptive Data Mining
Book 8 of 10: Computational Risk ManagementSeller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis.Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
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Add to basketHardcover. Condition: Brand New. 2nd edition. 130 pages. 9.50x6.25x0.50 inches. In Stock.
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US$ 250.87
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Add to basketPaperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: New.
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US$ 394.42
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Descriptive Data Mining
Book 7 of 10: Computational Risk ManagementLanguage: English
Published by Springer Nature Singapore Jul 2018, 2018
ISBN 10: 9811098476 ISBN 13: 9789811098475
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers an overview of knowledge management. It starts with an introduction to the subject, placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis. Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Both R and Rattle are free to students. Chapter 3 then describes market basket analysis, comparing it with more advanced models, and addresses the concept of lift. Subsequently, Chapter 4 describes smarketing RFM models and compares it with more advanced predictive models. Next, Chapter 5 describes association rules, including the APriori algorithm and provides software support from R. Chapter 6 covers cluster analysis, including software support from R (Rattle), KNIME, and WEKA, all of which are open source. Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output. Chapter 8 concludes the monograph.Using business-related data to demonstrate models, this descriptive book explains how methods work with some citations, but without detailed references. The data sets and software selected are widely available and can easily be accessed. 116 pp. Englisch.
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US$ 122.66
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes a discussion of descriptive analytic modelingCovers visualization in the context of data miningDemonstrates modeling with R and other open source software productsDav.
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US$ 183.38
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Add to basketCondition: New. Print on Demand pp. 116.
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Condition: New. PRINT ON DEMAND pp. 116.
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Descriptive Data Mining
Book 8 of 10: Computational Risk ManagementLanguage: English
Published by Springer, Springer Mai 2019, 2019
ISBN 10: 9811371806 ISBN 13: 9789811371806
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis.Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links. 144 pp. Englisch.
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US$ 163.97
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Ships from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a comprehensive overview of knowledge management, big data, and basic descriptive data mining methods and softwareIllustrates concepts with typical dataDemonstrates readily available open source software.
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Descriptive Data Mining
Book 8 of 10: Computational Risk ManagementLanguage: English
Published by Springer, Springer Mai 2019, 2019
ISBN 10: 9811371806 ISBN 13: 9789811371806
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 144 pp. Englisch.
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US$ 250.93
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Add to basketCondition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
