Discovering Knowledge in Data: An Introduction to Data Mining

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

Nyckelord: discovering knowledge in data; data mining; data mining guide; data mining text; data mining book; multivariate statistical analysis; variance analysis; chi-square procedures; cost-benefit analyses; time-series data analysis; R programming language; R language; R statistical programming; R statistical programming language; data analysis; data analysis techniques, Data Mining Statistics, Database & Data Warehousing Technologies, Data Mining Statistics, Database & Data Warehousing Technologies