Please use this identifier to cite or link to this item:
192.168.6.56/handle/123456789/88767Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Samson Abramsky, Karin Breitman, Chris Hankin, Dexter Kozen, Andrew Pitts, Hanne Riis Nielson, Steven Skiena and Iain Stewart | - |
| dc.contributor.author | Hannu, Karttu | - |
| dc.contributor.editor | Ian Mackie | - |
| dc.date.accessioned | 2020-05-27T08:40:46Z | - |
| dc.date.available | 2020-05-27T08:40:46Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.isbn | 978-1-4471-7307-6 | - |
| dc.identifier.uri | http://196.189.45.87:8080/handle/123456789/88767 | - |
| dc.description | This book is designed to be suitable for an introductory course at either un- dergraduate or masters level. It can be used as a textbook for a taught unit in a degree programme on potentially any of a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioin- formatics and Forensic Science. It is also suitable for use as a self-study book for those in technical or management positions who wish to gain an understanding of the subject that goes beyond the superficial. It goes well beyond the gener- alities of many introductory books on Data Mining but—unlike many other books—you will not need a degree and/or considerable fluency in Mathematics to understand it. | en |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer Science+Business Media, LLC 2010 | en_US |
| dc.subject | Principles of Data Mining | en_US |
| dc.title | Principles of Data Mining | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | Computer Science | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2016_Book_PrinciplesOfDataMining.pdf | 3.95 MB | Adobe PDF | View/Open |
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