Matignon Data Mining Using SAS Enterprise Miner

1. Auflage 2008
ISBN: 978-0-470-17142-4
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 584 Seiten, E-Book

Reihe: Wiley Series in Computational Statistics

The most thorough and up-to-date introduction to data miningtechniques using SAS Enterprise Miner.
The Sample, Explore, Modify, Model, and Assess (SEMMA)methodology of SAS Enterprise Miner is an extremely valuableanalytical tool for making critical business and marketingdecisions. Until now, there has been no single, authoritative bookthat explores every node relationship and pattern that is a part ofthe Enterprise Miner software with regard to SEMMA design and datamining analysis.
Data Mining Using SAS Enterprise Miner introduces readers to awide variety of data mining techniques and explains the purposeof-and reasoning behind-every node that is a part of the EnterpriseMiner software. Each chapter begins with a short introduction tothe assortment of statistics that is generated from the variousnodes in SAS Enterprise Miner v4.3, followed by detailedexplanations of configuration settings that are located within eachnode. Features of the book include:
* The exploration of node relationships and patterns using datafrom an assortment of computations, charts, and graphs commonlyused in SAS procedures
* A step-by-step approach to each node discussion, along with anassortment of illustrations that acquaint the reader with the SASEnterprise Miner working environment
* Descriptive detail of the powerful Score node and associatedSAS code, which showcases the important of managing, editing,executing, and creating custom-designed Score code for the benefitof fair and comprehensive business decision-making
* Complete coverage of the wide variety of statisticaltechniques that can be performed using the SEMMA nodes
* An accompanying Web site that provides downloadable Scorecode, training code, and data sets for further implementation,manipulation, and interpretation as well as SAS/IML softwareprogramming code
This book is a well-crafted study guide on the various methodsemployed to randomly sample, partition, graph, transform, filter,impute, replace, cluster, and process data as well as interactivelygroup and iteratively process data while performing a wide varietyof modeling techniques within the process flow of the SASEnterprise Miner software. Data Mining Using SAS Enterprise Mineris suitable as a supplemental text for advanced undergraduate andgraduate students of statistics and computer science and is also aninvaluable, all-encompassing guide to data mining for novicestatisticians and experts alike.

Autoren/Hrsg.

Matignon, Randall

Inhaltsverzeichnis

Introduction
Chapter 1: Sample Nodes 1
1.1 Input Data Source Node 3
1.2 Sampling Node 32
1.3 Data Partition Node 45
Chapter 2: Explore Nodes 55
2.1 Distribution Explorer Node 57
2.2 Multiplot Node 64
2.3 Insight Node 74
2.4 Association Node 75
2.5 Variable Selection Node 99
2.6 Link Analysis Node 120
Chapter 3: Modify Nodes 153
3.1 Data Set Attributes Node 155
3.2 Transform Variables Node 160
3.3 Filter Outliers Node 169
3.4 Replacement Node 178
3.5 Clustering Node 192
3.6 SOMiKohonen Node 227
3.7 Time Series Node 248
3.8 Interactive Grouping Node 261
Chapter 4: Model Nodes 277
4.1 Regression Node 279
4.2 Model Manager 320
4.3 Tree Node 324
4.4 Neural Network Node 355
4.5 PrincompiDmneural Node 420
4.6 User Defined Node 443
4.7 Ensemble Node 450
4.8 Memory-Based Reasoning Node 460
4.9 Two Stage Node 474
Chapter 5: Assess Nodes 489
5.1 Assessment Node 491
5.2 Reporter Node 511
Chapter 6: Scoring Nodes 515
6.1 Score Node 517
Chapter 7: Utility Nodes 525
7.1 Group Processing Node 527
7.2 Data Mining Database Node 537
7.3 SAS Code Node 541
7.4 Control point Node 552
7.5 Subdiagram Node 553
References 557
Index 560

Über Autor(innen)

Randall Matignon, MS, is Senior Clinical SAS / Microsoft Office VBA Programmer for Amgen, Inc. in San Francisco, California. He has over twenty years of experience as a statistical programmer and applications developer in the pharmaceutical, healthcare, and biotechnology industries, and he has a broad knowledge of several programming languages, including SAS, S-Plus, and PL-SQL.