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Data mining : = practical machine learning tools and techniques /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data mining :/ Ian H. Witten, Eibe Frank, Mark A. Hall.
Reminder of title:	practical machine learning tools and techniques /
Author:	Witten, I. H.
other author:	Frank, Eibe.
Published:	Burlington, MA :Morgan Kaufmann, : c2011.,
Description:	xxxiii, 629 p. :ill. ;24 cm.
[NT 15003449]:	Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.
Subject:	Data mining. -
ISBN:	9780123748560 (pbk.)

Data mining : = practical machine learning tools and techniques /
Witten, I. H.

Data mining :practical machine learning tools and techniques /Ian H. Witten, Eibe Frank, Mark A. Hall. - 3rd ed. - Burlington, MA :Morgan Kaufmann,c2011. - xxxiii, 629 p. :ill. ;24 cm. - [Morgan Kaufmann series in data management systems]. - Morgan Kaufmann series in data management systems..

Includes bibliographical references (p. 587-605) and index.

Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.

ISBN: 9780123748560 (pbk.)

LCCN: 2010039827Subjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / W58 2011

Dewey Class. No.: 006.3/12

Data mining : = practical machine learning tools and techniques /
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008 120229s2011 maua b 001 0 eng
010 $a 2010039827
020 $a 9780123748560 (pbk.)
020 $a 0123748569 (pbk.)
035 $a LIB-BW-101-004
035 $a (OCoLC)ocn262433473
035 $a 16490354
040 $a DLC $c DLC $d YDX $d BTCTA $d YDXCP $d BWX $d DEBSZ $d CDX $d IUL $d DLC
042 $a pcc
050 0 0 $a QA76.9.D343 $b W58 2011
082 0 0 $a 006.3/12 $2 22
100 1 $a Witten, I. H. $q (Ian H.) $3 1325351
245 1 0 $a Data mining : $b practical machine learning tools and techniques / $c Ian H. Witten, Eibe Frank, Mark A. Hall.
250 $a 3rd ed.
260 # $a Burlington, MA : $b Morgan Kaufmann, $c c2011.
300 $a xxxiii, 629 p. : $b ill. ; $c 24 cm.
490 1 $a [Morgan Kaufmann series in data management systems]
504 $a Includes bibliographical references (p. 587-605) and index.
505 0 # $a Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.
650 # 0 $a Data mining. $3 562972
700 1 # $a Frank, Eibe. $3 562970
700 1 # $a Hall, Mark A. $3 1325352
830 0 $a Morgan Kaufmann series in data management systems. $3 827306
906 $a 7 $b cbc $c orignew $d 1 $e ecip $f 20 $g y-gencatlg
925 0 $a acquire $b 2 shelf copies $x policy default
955 $b xh00 2010-10-05 $a xh07 2010-10-05 ONIX awaiting for author/edition info. $i xh07 2010-10-08 to Dewey $w rd14 2010-10-12 $a xe14 2011-04-20 2 copies rec'd., to CIP ver. $f xj06 2011-05-03 Z-CipVer Copy 1-2 to BCCD $e tc12 2011-12-06 bib. updates