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1. |
Record Nr. |
UNINA9910800168803321 |
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Titolo |
Oceanography and marine biology . Volume 53 : an annual review / / editors, R.N. Hughes, D.J. Hughes, I.P. Smith, A.C. Dale |
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Pubbl/distr/stampa |
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Boca Raton : , : CRC Press, , 2015 |
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ISBN |
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0-429-16235-9 |
1-4987-0546-4 |
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Descrizione fisica |
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1 online resource (308 p.) |
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Collana |
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Oceanography and marine biology. Volume 53 : an annual review |
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Disciplina |
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Soggetti |
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Oceanography |
Marine biology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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""Spatial, Temporal and Taxonomic Variation in Coral Growth�Implications for the Structure and Function of Coral Reef Ecosystems""""Back Cover"" |
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Sommario/riassunto |
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Ever-increasing interest in oceanography and marine biology and their relevance to global environmental issues create a demand for authoritative reviews summarizing the results of recent research. Oceanography and Marine Biology: An Annual Review has catered to this demand since its founding by the late Harold Barnes more than 50 years ago. Its objectives are to consider, annually, the basic areas of marine research, returning to them when appropriate in future volumes; to deal with subjects of special and topical importance; and to add new subjects as they arise.The favourable reception and c |
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2. |
Record Nr. |
UNINA9910350210003321 |
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Autore |
Olson David L |
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Titolo |
Descriptive Data Mining / / by David L. Olson, Georg Lauhoff |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[2nd ed. 2019.] |
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Descrizione fisica |
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1 online resource (XI, 130 p. 89 illus., 78 illus. in color.) |
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Collana |
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Computational Risk Management, , 2191-1444 |
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Disciplina |
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Soggetti |
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Quantitative research |
Data mining |
Financial risk management |
Data Analysis and Big Data |
Data Mining and Knowledge Discovery |
Risk Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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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. |
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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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