Introduction to data mining / Pang-Ning Tan, Michael Steinbach, Vipin Kumar.
Material type: TextPublisher: Harlow : Pearson Education, 2016Edition: 2nd editionDescription: 792 pages ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780273769224 (pbk.) :Subject(s): Data mining | Computers and IT | Computers and ITDDC classification: 006.3'12 Summary: Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.Item type | Current library | Home library | Class number | Status | Date due | Barcode | Item reservations | |
---|---|---|---|---|---|---|---|---|
Book | Paul Hamlyn Library | Paul Hamlyn Library | 006.312 TAN (Browse shelf(Opens below)) | Available | 06480713 | |||
Book | Paul Hamlyn Library | Paul Hamlyn Library | 006.312 TAN (Browse shelf(Opens below)) | Available | 06480721 | |||
Book | Paul Hamlyn Library | Paul Hamlyn Library | 006.312 TAN (Browse shelf(Opens below)) | Available | 0648073X |
Total reservations: 0
Browsing Paul Hamlyn Library shelves Close shelf browser (Hides shelf browser)
006.312 TAN Introduction to Data Mining / | 006.312 TAN Introduction to data mining / | 006.312 TAN Introduction to data mining / | 006.312 TAN Introduction to data mining / | 006.312 TEX Text Processing with GATE (Version6+. | 006.312 WIT Data mining : practical machine learning tools and techniques. | 006.312 WIT Data mining : practical machine learning tools and techniques. |
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
Specialized.
There are no comments on this title.
Log in to your account to post a comment.