Data preparation for data mining using SAS

著者

    • Refaat, Mamdouh

書誌事項

Data preparation for data mining using SAS

Mamdouh Refaat

(The Morgan Kaufmann series in data management systems)

Morgan Kaufmann Publishers is an imprint of Elsevier, c2007

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to" information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.

目次

Contents 1 Introduction 2 Tasks and Data Flow 3 Review of Data Mining Modeling Techniques 4 SAS Macros: A Quick Start 5 Data Acquisition and Integration 6 Integrity Checks 8 Sampling and Partitioning 9 Data Transformations 10 Binning and Reduction of Cardinality11 Treatment of Missing Values 12 Predictive Power and Variable Reduction I 13 Analysis of Nominal and Ordinal Variables 14 Analysis of Continuous Variables 15 Principal Component Analysis (PCA) 2 16 Factor Analysis 17 Predictive Power and Variable Reduction II 18 Putting it All Together A Listing of SAS Macros

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詳細情報

  • NII書誌ID(NCID)
    BA78801307
  • ISBN
    • 9780123735775
  • LCCN
    2006023681
  • 出版国コード
    ne
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Amsterdam ; Tokyo
  • ページ数/冊数
    xxi, 399 p.
  • 大きさ
    24 cm.
  • 付属資料
    1 CD-ROM(12cm)
  • 分類
  • 件名
  • 親書誌ID
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