Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall

Von:

Witten, Ian H, 1947- [aut]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: [Morgan Kaufmann series in data management systems]Verlag: San Francisco : Elsevier Science & Technology, c2011Auflage: 3rd ed (Online-Ausg.)Beschreibung: Online-Ressource (1 online resource (xxxiii, 629 p.)) : illISBN:

9781282953888
1282953885
9780080890364

Schlagwörter:

Andere physische Formen: 0123748569 | 9780080890364 | 9780123748560 | 1282953753 | Erscheint auch als: Data mining. Druck-Ausgabe 3. ed. Amsterdam : Morgan Kaufmann, Elsevier, 2011. XXXIII, 629 S.DDC-Klassifikation:

006.312
006.3/12 22
006.3

RVK: RVK: ST 530 | ST 270Local classification: Lokale Notation: inf 5.6LOC-Klassifikation:

QA76.9.D343

Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: Front cover -- Data Mining: Practical Machine Learning Tools and Techniques, 3/e -- Copyright page -- Table of Contents -- List of Figures -- List of Tables -- Preface -- Updated and revised content -- Second Edition -- Third Edition -- Acknowledgments -- About the Authors -- I Introduction to Data Mining -- 1 What's It All About? -- 1.1 Data mining and machine learning -- Describing Structural Patterns -- Machine Learning -- Data Mining -- 1.2 Simple examples: the weather and other problems -- The Weather Problem -- Contact Lenses: An Idealized Problem -- Irises: A Classic Numeric Dataset -- CPU Performance: Introducing Numeric Prediction -- Labor Negotiations: A More Realistic Example -- Soybean Classification: A Classic Machine Learning Success -- 1.3 Fielded applications -- Web Mining -- Decisions Involving Judgment -- Screening Images -- Load Forecasting -- Diagnosis -- Marketing and Sales -- Other Applications -- 1.4 Machine learning and statistics -- 1.5 Generalization as search -- 1.6 Data mining and ethics -- Reidentification -- Using Personal Information -- Wider Issues -- 1.7 Further reading -- 2 Input: -- 2.1 What's a concept? -- 2.2 What's in an example? -- Relations -- Other Example Types -- 2.3 What's in an attribute? -- 2.4 Preparing the input -- Gathering the Data Together -- ARFF Format -- Sparse Data -- Attribute Types -- Missing Values -- Inaccurate Values -- Getting to Know Your Data -- 2.5 Further reading -- 3 Output: -- 3.1 Tables -- 3.2 Linear models -- 3.3 Trees -- 3.4 Rules -- Classification Rules -- Association Rules -- Rules with Exceptions -- More Expressive Rules -- 3.5 Instance-based representation -- 3.6 Clusters -- 3.7 Further reading -- 4 Algorithms: -- 4.1 InFerring rudimentary rules -- Missing Values and Numeric Attributes -- Discussion -- 4.2 Statistical modeling -- Missing Values and Numeric Attributes.PPN: PPN: 809190168Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE

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