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Discovering Knowledge in Data
An Introduction to Data Mining
Daniel T. Larose, Chantal D. Larose
- English
- PDF
- Available on iOS & Android
Discovering Knowledge in Data
An Introduction to Data Mining
Daniel T. Larose, Chantal D. Larose
About This Book
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 for university instructorswho adopt the book
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Table of contents
- DISCOVERING KNOWLEDGE IN DATA
- Contents
- Preface
- 1 An Introduction to Data Mining
- 2 Data Preprocessing
- 3 Exploratory Data Analysis
- 4 Univariate Statistical Analysis
- 5 Multivariate Statistics
- 6 Preparing to Model the Data
- 7 k-Nearest Neighbor Algorithm
- 8 Decision Trees
- 9 Neural Networks
- 10 Hierarchical and k-Means Clustering
- 11 Kohonen Networks
- 12 Association Rules
- 13 Imputation of Missing Data
- 14 Model Evaluation Techniques
- Appendix Data Summarization and Visualization
- Index