Descriptive Data Mining / / by David L. Olson
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| Autore: |
Olson David L
|
| Titolo: |
Descriptive Data Mining / / by David L. Olson
|
| Pubblicazione: | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Edizione: | 1st ed. 2017. |
| Descrizione fisica: | 1 online resource (XI, 116 p. 63 illus., 60 illus. in color.) |
| Disciplina: | 006.312 |
| Soggetto topico: | Big data |
| Data mining | |
| Risk management | |
| Big Data/Analytics | |
| Data Mining and Knowledge Discovery | |
| Risk Management | |
| Nota di bibliografia: | Includes bibliographical references at the end of each chapters and index. |
| Nota di contenuto: | Chapter 1 Knowledge Management -- Chapter 2: Data Visualization -- Chapter 3 Market Basket Analysis -- Chapter 4 Recency Frequency and Monetary Model -- Chapter 5 Association Rules -- Chapter 6 Cluster Analysis -- Chapter 7 Link Analysis -- Chapter 7 Link Analysis -- Chapter 8 Descriptive Data Mining -- References -- Index. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Descriptive Data Mining |
| ISBN: | 981-10-3340-4 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910155525703321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Computational Risk Management, . 2191-1436