01469nam a2200241 a 4500001001400000003000700014005001700021008004100038020002500079020002200104040000800126100001900134245011000153250001200263260004600275300003700321490005400358504006300412505070200475650001601177700001601193700001801209vtls000056575KUKTEM20251125093043.0111205t2011    maua   f      001 0 eng d  a9780123748560 (pbk.)  a0123748569 (pbk.)  aUMP1 aWitten, Ian H.10aData mining :bpractical machine learning tools and techniques /cIan H. Witten, Eibe Frank, Mark A. Hall  a3rd ed.  aBurlington, MA. :bMorgan Kaufmann,c2011  axxxiii, 629 p. :bill. ;c24 cm.0 aMorgan Kaufmann series in data management systems  aIncludes bibliographical references (p. 587-605) and index0 aPart 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 0aData mining1 aFrank, Eibe1 aHall, Mark A.