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Informationen zum Autor 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. Klappentext Data Mining Using SAS(r) Enterprise Miner introduces the reader to a wide variety of data mining techniques in SAS(r) Enterprise Miner. This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of Enterprise Miner with regard to SEMMA design and data mining analysis. Each chapter starts with a short introduction to the assortment of statistics that are generated from the various Enterprise Miner nodes, followed by detailed explanations of configuration settings that are located within each node. The end result of the author's meticulous presentation is a well crafted study guide on the various methods that one employs to both randomly sample and partition data within the process flow of SAS(r) Enterprise Miner. Zusammenfassung Data Mining Using SAS(r) Enterprise Miner introduces the reader to a wide variety of data mining techniques in SAS(r) Enterprise Miner. This first-of-a-kind book explains the purpose of -- and reasoning behind -- every node that is a part of Enterprise Miner with regard to SEMMA design and data mining analysis. 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 ...
