Data mining and data warehousing : principles and practical techniques
By: Bhatia, Parteek
Material type: BookPublisher: New York, NY : Cambridge University Press, c2019.Description: xxix, 477 p. : ill. ; 25 cm.ISBN: 9781108727747Subject(s): Data mining -- Textbooks | Data warehousing -- TextbooksDDC classification: 006.312 BH DA Online resources: Location MapItem type | Home library | Call number | Status | Notes | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 006.312 BH DA (Browse shelf) | Available | Feb2020 | T0063391 |
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006.312 AN AL Analysis of social media and ubiquitous data : | 006.312 AN ST Statistics for big data for dummies | 006.312 AN TI Anticipating future innovation pathways through large data analysis / | 006.312 BH DA Data mining and data warehousing : | 006.312 CO LL Collaborative filtering using data mining and analysis | 006.312 CO MP Compromised data : | 006.312 CU GO Google analytics / |
Includes bibliographical references and index.
Beginning with machine learning -- Introduction to data mining -- Beginning with Weka and R language -- Data preprocessing -- Classification -- Implementing classification in Weka and R -- Cluster analysis -- Implementing clustering with Weka and R -- Association mining -- Implementing association mining with Weka and R -- Web mining and search engines -- Data warehouse -- Data warehouse schema -- Online analytical processing -- Big data and NoSQL.
"This 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. It 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 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. Unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding"--