Practical Guide to Data Mining for Business and Industry:
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is c...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Somerset
Wiley
2014
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Zusammenfassung: | Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (325 pages) |
| ISBN: | 9781118763728 9781118763704 |
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| spelling | Ahlemeyer-Stubbe, Andrea Verfasser aut Practical Guide to Data Mining for Business and Industry 1st ed Somerset Wiley 2014 © 2014 1 online resource (325 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest Datenverarbeitung Mathematisches Modell Data mining Management -- Mathematical models Marketing -- Data processing Coleman, Shirley Sonstige oth Erscheint auch als Druck-Ausgabe Ahlemeyer-Stubbe, Andrea Practical Guide to Data Mining for Business and Industry |
| spellingShingle | Ahlemeyer-Stubbe, Andrea Practical Guide to Data Mining for Business and Industry Datenverarbeitung Mathematisches Modell Data mining Management -- Mathematical models Marketing -- Data processing |
| title | Practical Guide to Data Mining for Business and Industry |
| title_auth | Practical Guide to Data Mining for Business and Industry |
| title_exact_search | Practical Guide to Data Mining for Business and Industry |
| title_full | Practical Guide to Data Mining for Business and Industry |
| title_fullStr | Practical Guide to Data Mining for Business and Industry |
| title_full_unstemmed | Practical Guide to Data Mining for Business and Industry |
| title_short | Practical Guide to Data Mining for Business and Industry |
| title_sort | practical guide to data mining for business and industry |
| topic | Datenverarbeitung Mathematisches Modell Data mining Management -- Mathematical models Marketing -- Data processing |
| topic_facet | Datenverarbeitung Mathematisches Modell Data mining Management -- Mathematical models Marketing -- Data processing |
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