Data Mining Using SAS Enterprise Miner Wiley Series in Computational Statistics 1st Edition by Randall Matignon – Ebook PDF Instant Download/Delivery: 0470171421, 9780470171424
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Product details:
ISBN 10: 0470171421
ISBN 13: 9780470171424
Author: Randall Matignon
The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner.
The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis.
Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node. Features of the book include:
- The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures
- A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment
- Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making
- Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes
- An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code
This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike
Data Mining Using SAS Enterprise Miner Wiley Series in Computational Statistics 1st Table of contents:
Chapter 1: Sample Nodes
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1.1 Input Data Source Node
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1.2 Sampling Node
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1.3 Data Partition Node
Chapter 2: Explore Nodes
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2.1 Distribution Explorer Node
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2.2 Multiplot Node
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2.3 Insight Node
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2.4 Association Node
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2.5 Variable Selection Node
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2.6 Link Analysis Node
Chapter 3: Modify Nodes
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3.1 Data Set Attributes Node
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3.2 Transform Variables Node
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3.3 Filter Outliers Node
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3.4 Replacement Node
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3.5 Clustering Node
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3.6 SOMiKohonen Node
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3.7 Time Series Node
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3.8 Interactive Grouping Node
Chapter 4: Model Nodes
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4.1 Regression Node
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4.2 Model Manager
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4.3 Tree Node
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4.4 Neural Network Node
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4.5 PrincompiDmneural Node
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4.6 User Defined Node
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4.7 Ensemble Node
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4.8 Memory-Based Reasoning Node
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4.9 Two Stage Node
Chapter 5: Assess Nodes
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5.1 Assessment Node
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5.2 Reporter Node
Chapter 6: Scoring Nodes
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6.1 Score Node
Chapter 7: Utility Nodes
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7.1 Group Processing Node
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7.2 Data Mining Database Node
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7.3 SAS Code Node
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7.4 Control point Node
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7.5 Subdiagram Node
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Tags: Randall Matignon, Data Mining, Computational Statistics


