02065cam a2200313 i 450000100090000000300040000900500170001300800410003001000170007102000400008804000280012804200080015605000270016408200170019110000300020824500950023826400840033330000600041733600210047733700250049833800230052350400510054652010170059765000280161465000330164290600450167594200140172099900170173420922191OSt20260113150054.0190403s2019    enka     b    001 0 eng c  a  2019013358  a9781108727747q(pbk. ;qalk. paper)  acukbengecukccukdDLC  apcc00aQA76.9.D343bB435 201900a006.3/122231 aBhatia, Parteek,eauthor.10aData Mining and Data Warehousing :bPrinciples and Practical Techniques /cParteek Bhatia. 1aCambridge, United Kingdom ;aNew York, NY :bCambridge University Press,c2019.  axxxiv, 477 pages :billustrations (some color) ;c24 cm  atext2rdacontent  aunmediated2rdamedia  avolume2rdacarrier  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"--cProvided by publisher. 0aData miningvTextbooks. 0aData warehousingvTextbooks.  a7bcbccorignewd1eecipf20gy-gencatlg  2lcccBOOK  c20330d20330