Custom cover image
Custom cover image

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

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: [Morgan Kaufmann series in data management systems]Publisher: San Francisco : Elsevier Science & Technology, c2011Edition: 3rd ed (Online-Ausg.)Description: Online-Ressource (1 online resource (xxxiii, 629 p.)) : illISBN:
  • 9781282953888
  • 1282953885
  • 9780080890364
Subject(s): Additional physical formats: 0123748569 | 9780080890364 | 9780123748560 | 1282953753 | Erscheint auch als: Data mining. Druck-Ausgabe 3. ed. Amsterdam : Morgan Kaufmann, Elsevier, 2011. XXXIII, 629 S.DDC classification:
  • 006.312
  • 006.3/12 22
  • 006.3
RVK: RVK: ST 530 | ST 270Local classification: Lokale Notation: inf 5.6LOC classification:
  • QA76.9.D343
Online resources: Summary: 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
No physical items for this record
KIT – The University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT