소장자료
LDR | 01548cam a22003258a 4500 | ||
001 | 0091872294▲ | ||
005 | 20180519213253▲ | ||
008 | 100225s2010 njua b 001 0 eng ▲ | ||
010 | ▼a2010005152▲ | ||
020 | ▼a9780470526828 (cloth)▲ | ||
035 | ▼a(KERIS)REF000015693402▲ | ||
040 | ▼aDLC▼cDLC▼d221016▲ | ||
050 | 0 | 0 | ▼aHF5548.2▼b.S44843 2010▲ |
082 | 0 | 0 | ▼a005.54▼221▲ |
090 | ▼a005.54▼bS558d2▲ | ||
100 | 1 | ▼aShmueli, Galit,▼d1971-▲ | |
245 | 1 | 0 | ▼aData mining for business intelligence :▼bconcepts, techniques, and applications in Microsoft Office Excel with XLMiner /▼cGalit Shmueli, Nitin R. Patel, Peter C. Bruce.▲ |
250 | ▼a2nd ed.▲ | ||
260 | ▼aHoboken, N.J. :▼bWiley,▼c2010.▲ | ||
263 | ▼a1007▲ | ||
300 | ▼axxiv, 404 p. :▼bill. ;▼c26 cm.▲ | ||
504 | ▼aIncludes bibliographical references and index.▲ | ||
505 | 0 | ▼aWhat is data mining? -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating classification and predictive performance -- Multiple linear regression -- knearest neighbors (kNN) -- Na've bayes -- Classification and regression trees -- Logistic regression.▲ | |
520 | ▼aData Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models.▲ | ||
630 | 0 | 0 | ▼aMicrosoft Excel (Computer file)▲ |
650 | 0 | ▼aBusiness▼xData processing.▲ | |
650 | 0 | ▼aData mining.▲ | |
700 | 1 | ▼aPatel, Nitin R.▼q(Nitin Ratilal)▲ | |
700 | 1 | ▼aBruce, Peter C.,▼d1953-▲ | |
999 | ▼c정영주▲ |
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Data mining for business intelligence :concepts, techniques, and applications in Microsoft Office Excel with XLMiner
자료유형
국외단행본
서명/책임사항
Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner / Galit Shmueli, Nitin R. Patel, Peter C. Bruce.
개인저자
판사항
2nd ed.
발행사항
Hoboken, N.J. : Wiley , 2010.
형태사항
xxiv, 404 p. : ill. ; 26 cm.
서지주기
Includes bibliographical references and index.
내용주기
What is data mining? -- Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating classification and predictive performance -- Multiple linear regression -- knearest neighbors (kNN) -- Na've bayes -- Classification and regression trees -- Logistic regression.
요약주기
Data Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models.
ISBN
9780470526828 (cloth)
청구기호
005.54 S558d2
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