Discovering knowledge in data: An introduction to data mining

By: Larose, Daniel T [Author] | Larose, Chantal D [Co-Author]Material type: Text

TextLanguage: English Publication details: New Jersey: Wiley Data and Cybersecurity, 2014. Edition: 2nd edDescription: xviii, 316pISBN: 9781118873588Subject(s): Introduction to Data Mining | Data Preprocessing and Exploratory Data Analysis | Univariate Statistical Analysis and Multivariate StatisticsDDC classification: 006.3 Online resources: Click here to access online

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Item type	Current library	Call number	Status	Date due	Barcode
e-Books	Dr. S. R. Ranganathan Library Ebook (Online Access)	006.3 (Browse shelf(Opens below))	Available		EB0132

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Previous	005.9065 Cyber intelligence-driven risk: how to build and use cyber intelligence for business risk decisions	006.12 Networking fundamentals	006.133 NoSQL data models: trends and challenges	006.3 Discovering knowledge in data: An introduction to data mining	006.3 Intelligent data analysis: from data gathering to data comprehension	006.3 Machine learning theory and applications: hands-on use cases with python on classical and quantum machines	006.3 Making sense of data I: A practical guide to exploratory data analysis and data mining	Next

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