000 02233nam a2200241 4500
005 20250731101304.0
008 250731b2014|||||||| |||| 00| 0 eng d
020 _a9781118873588
041 _aEnglish
082 _a006.3
100 _aLarose, Daniel T.
_eAuthor
_96897
100 _aLarose, Chantal D.
_eCo-Author
_96898
245 _aDiscovering knowledge in data: An introduction to data mining
250 _a2nd ed.
260 _aNew Jersey:
_bWiley Data and Cybersecurity,
_c2014.
300 _axviii, 316p.
500 _aThe 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
650 _aIntroduction to Data Mining
_96899
650 _aData Preprocessing and Exploratory Data Analysis
_96900
650 _aUnivariate Statistical Analysis and Multivariate Statistics
_96901
856 _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=10066951
942 _cEB
999 _c1708
_d1708