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Data Mining : a Tutorial-Based Primer, Second Edition.
"Provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a fe...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton :
CRC Press,
2017.
|
| Edición: | 2nd ed. |
| Colección: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Roiger, Richard J. | |
| 245 | 1 | 0 | |a Data Mining : |b a Tutorial-Based Primer, Second Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Boca Raton : |b CRC Press, |c 2017. | ||
| 300 | |a 1 online resource (530 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC Data Mining and Knowledge Discovery Series | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Half Title; Title Page; Copyright Page; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Author; Section I: Data Mining Fundamentals; Chapter 1. Data Mining: A First View; CHAPTER OBJECTIVES; 1.1 DATA SCIENCE, ANALYTICS, MINING, AND KNOWLEDGE DISCOVERY IN DATABASES; 1.1.1 Data Science and Analytics; 1.1.2 Data Mining; 1.1.3 Data Science versus Knowledge Discovery in Databases; 1.2 WHAT CAN COMPUTERS LEARN?; 1.2.1 Three Concept Views; 1.2.1.1 The Classical View; 1.2.1.2 The Probabilistic View; 1.2.1.3 The Exemplar View; 1.2.2 Supervised Learning. | |
| 505 | 8 | |a 1.2.3 Supervised Learning: A Decision Tree Example1.2.4 Unsupervised Clustering; 1.3 IS DATA MINING APPROPRIATE FOR MY PROBLEM?; 1.3.1 Data Mining or Data Query?; 1.3.2 Data Mining versus Data Query: An Example; 1.4 DATA MINING OR KNOWLEDGE ENGINEERING?; 1.5 A NEAREST NEIGHBOR APPROACH; 1.6 A PROCESS MODEL FOR DATA MINING; 1.6.1 Acquiring Data; 1.6.1.1 The Data Warehouse; 1.6.1.2 Relational Databases and Flat Files; 1.6.1.3 Distributed Data Access; 1.6.2 Data Preprocessing; 1.6.3 Mining the Data; 1.6.4 Interpreting the Results; 1.6.5 Result Application. | |
| 505 | 8 | |a 1.7 DATA MINING, BIG DATA, AND CLOUD COMPUTING1.7.1 Hadoop; 1.7.2 Cloud Computing; 1.8 DATA MINING ETHICS; 1.9 INTRINSIC VALUE AND CUSTOMER CHURN; 1.10 CHAPTER SUMMARY; 1.11 KEY TERMS; Chapter 2. Data Mining: A Closer Look; CHAPTER OBJECTIVES; 2.1 DATA MINING STRATEGIES; 2.1.1 Classicfiation; 2.1.2 Estimation; 2.1.3 Prediction; 2.1.4 Unsupervised Clustering; 2.1.5 Market Basket Analysis; 2.2 SUPERVISED DATA MINING TECHNIQUES; 2.2.1 The Credit Card Promotion Database; 2.2.2 Rule-Based Techniques; 2.2.3 Neural Networks; 2.2.4 Statistical Regression; 2.3 ASSOCIATION RULES. | |
| 505 | 8 | |a 2.4 CLUSTERING TECHNIQUES2.5 EVALUATING PERFORMANCE; 2.5.1 Evaluating Supervised Learner Models; 2.5.2 Two-Class Error Analysis; 2.5.3 Evaluating Numeric Output; 2.5.4 Comparing Models by Measuring Lift; 2.5.5 Unsupervised Model Evaluation; 2.6 CHAPTER SUMMARY; 2.7 KEY TERMS; Chapter 3. Basic Data Mining Techniques; CHAPTER OBJECTIVES; 3.1 DECISION TREES; 3.1.1 An Algorithm for Building Decision Trees; 3.1.2 Decision Trees for the Credit Card Promotion Database; 3.1.3 Decision Tree Rules; 3.1.4 Other Methods for Building Decision Trees; 3.1.5 General Considerations. | |
| 505 | 8 | |a 3.2 A BASIC COVERING RULE ALGORITHM3.3 GENERATING ASSOCIATION RULES; 3.3.1 Confidence and Support; 3.3.2 Mining Association Rules: An Example; 3.3.3 General Considerations; 3.4 THE K-MEANS ALGORITHM; 3.4.1 An Example Using K-means; 3.4.2 General Considerations; 3.5 GENETIC LEARNING; 3.5.1 Genetic Algorithms and Supervised Learning; 3.5.2 General Considerations; 3.6 CHOOSING A DATA MINING TECHNIQUE; 3.7 CHAPTER SUMMARY; 3.8 KEY TERMS; Section II: Tools for Knowledge Discovery; Chapter 4. Weka-An Environment for Knowledge Discovery; CHAPTER OBJECTIVES; 4.1 GETTING STARTED WITH WEKA. | |
| 500 | |a 4.2 BUILDING DECISION TREES. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well known software tools."-- |c Cover page 4 | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a Data mining |2 fast | |
| 655 | 7 | |a Online-Ressource. |2 gnd-carrier | |
| 758 | |i has work: |a Data Mining (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYdrBdh6B9rpFvtQ8VvMpq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Roiger, Richard J. |t Data Mining : A Tutorial-Based Primer, Second Edition. |d Boca Raton : CRC Press, ©2017 |z 9781498763974 |
| 830 | 0 | |a Chapman & Hall/CRC data mining and knowledge discovery series. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4947407 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH32775395 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL4947407 | ||
| 994 | |a 92 |b IZTAP | ||