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210123 ||| eng |
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|a 9781482212389
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|a 9781336194304
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|a 1336194308
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|a 1482212382
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4 |
|a QA76.9.D3
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| 100 |
1 |
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|a Osei-Bryson, Kweku-Muata
|e editor
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| 245 |
0 |
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|a Knowledge discovery process and methods to enhance organizational performance
|c edited by Kweku-Muata Osei-Bryson, Corlane Barclay
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| 260 |
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|a Boca Raton, FL
|b CRC Press
|c 2015
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| 300 |
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|a xviii, 375 pages
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|a Section 1. Foundations -- section 2. KDDM techniques -- section 3. KDDM and organizational performance -- section 4. Applications of KDDM -- section 5. Data mining algorithms
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|a Includes bibliographical references
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| 653 |
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|a Data mining / fast
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| 653 |
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|a Bases de données / Interrogation
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| 653 |
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|a Querying (Computer science) / fast
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| 653 |
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|a Bases de données / Gestion
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| 653 |
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|a Business and Management / ukslc
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| 653 |
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|a Querying (Computer science) / http://id.loc.gov/authorities/subjects/sh2005008252
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| 653 |
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|a COMPUTERS / Databases / General / bisacsh
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| 653 |
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|a online searching / aat
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| 653 |
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|a Database searching / fast
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| 653 |
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|a Database management / http://id.loc.gov/authorities/subjects/sh85035848
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| 653 |
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|a Database searching / http://id.loc.gov/authorities/subjects/sh86007858
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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| 653 |
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|a Exploration de données (Informatique)
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| 653 |
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|a Database management / fast
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| 700 |
1 |
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|a Barclay, Corlane
|e editor
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 024 |
8 |
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|a 10.1201/b18231
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| 776 |
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|z 9781138894259
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| 776 |
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|z 1482212382
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| 776 |
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|z 9781482212365
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| 776 |
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|z 9781482212389
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| 776 |
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|z 1482212366
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781482212365/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 658
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|a 500
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|a 005.7565
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|a 330
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| 520 |
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|a This book explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to implement. It details powerful strategies, models, and techniques for managing the full cycle of knowledge discovery projects. It supplies a process-centric view of how to implement successful data mining projects through the use of the KDDM process and discusses the implications of data mining including security, privacy, ethical and legal considerations. This resource includes case study examples of KDDM in businesses and governments; details key requirements for developing robust data mining objectives that are aligned with strategic business objectives; outlines critical success factors for KDDM projects as well as the impact of poor quality data or inaccessibility to data on KDDM projects. --
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