Data Mining and Data Warehousing: Principles and Practical Techniques

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Parteek BhatiaCambridge University PressEdition: Illustrated, 8/15/2019EAN 9781108727747, ISBN10: 1108727743Paperback, 512 pages, 24.6 x 18.9 x 3.1 cmLanguage: EnglishWritten in lu cid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.PrefaceAcknowledgementDedication1. Beginning with machine learning2. Introduction to data mining3. Beginning with Weka and R language4. Data pre-processing5. Classification6. Implementing classification in Weka and R7. Cluster analysis8. Implementing clustering with Weka and R9. Association mining10. Implementing association mining with Weka and R11. Web mining and search engine12. Operational data store and data warehouse13. Data warehouse schema14. Online analytical processing15. Big data and NoSQLReferenceIndex. Show more