Introduction to data mining / Tan, Steinbach, Kumar.

By:

Tan, Pang-Ning [author.]

Contributor(s):

Material type: Text

TextSeries: Pearson custom libraryPublisher: Harlow, Essex : Pearson [2014]Edition: Pearson new international edition; First editionDescription: ii, 732 pages : illustrations (black and white) ; 28 cmISBN:

9781292026152 (pbk.) :
9781292026152 (pbk.) :

Subject(s):

DDC classification:

006.312 TAN

LOC classification:

QA76.9.D343 T35 2014

Contents:

1.Introduction. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 2.Data. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 3.Exploring data. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 4.Classification: basic concepts, decision trees, and model evalution. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 5.Classification: alternative techniques. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 8.Cluster analysis: additional issues and algorithms. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 9.Cluster analysis: additonal issues and algorithms. Pang-Ning Tan/Michael Steinbach/Vipin Kumar -- 10. Anomaly detection. Pang-Ning Tan/Michael Steinbach/Vipin Kumar.

Summary: 'Introduction to Data Mining' presents fundamental concepts and algorithms for those learning data mining. 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.

Holdings ( 1 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Long Loan	TUS: Midlands, Main Library Athlone General Lending	006.312 TAN (Browse shelf(Opens below))	1	Available		216915

Includes bibliographical references and index.

'Introduction to Data Mining' presents fundamental concepts and algorithms for those learning data mining. 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.