01771nam a2200217 i 450000100090000000300090000900500170001800800410003501000170007602000400009304000460013308200160017910000300019524500950022526000680032030000600038850400510044852009930049965000280149265000330152020922191BD-KhUET20241127104130.0241127s2019    enka     b    001 0 eng c  a  2019013358  a9781108727747q(pbk. ;qalk. paper)  aLBSOR/DLCbengerdacLBSORdDLCdBD-KhUET00a006.3122231 aBhatia, Parteek,eauthor.10aData mining and data warehousing :bprinciples and practical techniques /cParteek Bhatia.  aNew York :bCambridge University Press,cc2019 [reprinted 2022]  axxxiv, 477 pages :billustrations (some color) ;c24 cm  aIncludes bibliographical references and index.  a"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"-- 0aData miningvTextbooks. 0aData warehousingvTextbooks.