Discovering Knowledge In Data: An Introduction To Data Mining, 2nd Edition

         


Discovering Knowledge in Data: An Introduction to Data Mining (Wiley Series on Methods and Applications in Data Mining)
By 作者: Daniel T. Larose – Chantal D. Larose
ISBN-10 书号: 0470908742
ISBN-13 书号: 9780470908747
Edition 版本: 2
Release Finelybook 出版日期: 2014-07-08
pages 页数: (336 )

$96.75
Book Description to Finelybook sorting
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.
This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining.

The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.
Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization
Offers extensive coverage of the R statistical programming language
Contains 280 end-of-chapter exercises
Includes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book
Chapter 1:An Introduction to Data Mining
Chapter 2:Data Preprocessing
Chapter 3:Exploratory Data Analysis
Chapter 4:Univariate Statistical Analysis
Chapter 5:Multivariate Statistics
Chapter 6:Preparing to Model the Data
Chapter 7:k-Nearest Neighbor Algorithm
Chapter 8:Decision Trees
Chapter 9.Neural Networks
Chapter 10:Hierarchical and k-Means Clustering
Chapter 11:Kohonen Networks
Chapter 12:Association Rules
Chapter 13:Imputation of Missing Data
Chapter 14:Model Evaluation Techniques
Appendix Data Summarization and Visualization
Index
End User License Agreement


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