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 instructors who adopt the 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 instructors who adopt the book
This Special Issue includes works on linking data and learning, using student-assessment results, data mining, data decision making for teachers, identifying student's needs with technology and data...
Since 1981, the British National Conferences on Databases (BNCOD) have p- vided a forum for database researchers to report the latest progress and explore new ideas. Over the last 28 years, BNCOD has...
Discovery of Intrinsic Clustering in Spatial Data.- Statistical Approach to the Identification of Separation Surface for Spatial Data.- Algorithmic Approach to the Identification of Classification...
Within thelastfewyears,datawarehousingandknowledgediscoverytechnology has established itself as a key technology for enterprises that wish to improve the quality of the results obtained from data...
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