SAS企业矿工数据挖掘实战指南

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"Data Mining Using SAS Enterprise Miner - SAS EM教程，英文原版，适合作为初学者的入门资料。" 在数据分析领域，SAS Enterprise Miner（SAS EM）是一款强大的数据挖掘工具，它允许用户通过图形用户界面（GUI）进行数据预处理、特征工程、建模和模型评估等一系列数据挖掘任务。本书《Data Mining Using SAS Enterprise Miner: A Case Study Approach》第二版，由SAS Institute Inc.出版，旨在以案例研究的方式帮助读者理解并掌握如何使用SAS EM。本书涵盖了以下关键知识点： 1. **数据挖掘基础**：介绍数据挖掘的基本概念、目标和过程，包括探索性数据分析、数据预处理的重要性以及不同类型的挖掘任务（如分类、回归、聚类等）。 2. **SAS EM工作流程**：详细解释了SAS EM的工作环境，如节点网络、流图构建和项目管理。读者将学习如何使用各个节点来执行不同的任务，如数据导入、数据转换、变量选择等。 3. **预处理技术**：讨论数据清洗、缺失值处理、异常值检测和数据标准化等预处理步骤，这些都是确保数据质量和模型准确性的关键。 4. **建模技术**：涵盖多种数据挖掘模型，如决策树、随机森林、支持向量机、神经网络、贝叶斯网络等。每个模型都会结合实际案例进行解释和应用。 5. **模型评估与优化**：介绍模型验证方法，如交叉验证、ROC曲线和AUC，以及如何通过调整参数来优化模型性能。 6. **案例研究**：书中的案例研究是学习SAS EM实战操作的宝贵资源。这些案例涉及金融、市场预测、医疗保健等多个领域，有助于读者理解如何将理论知识应用于实际问题解决。 7. **结果解释与报告**：教导读者如何理解和解读SAS EM生成的输出结果，并如何将这些结果转化为易于理解的报告，以供决策者参考。通过本书的学习，初学者不仅可以掌握SAS EM的基本操作，还能深入理解数据挖掘的整个生命周期，从而提升数据分析技能。同时，书中强调的实际案例将帮助读者将理论知识与实践经验相结合，从而在实际工作中更加得心应手。对于希望在数据分析领域使用SAS EM工具的人来说，这是一本不可多得的参考资料。

Overview of the Nodes 11

The Interactive Grouping node enables you to interactively group variable values into

classes. Statistical and plotted information can be interactively rearranged as you

explore various variable groupings. The Interactive Grouping node requires a binary

target variable.

Model Nodes

The Regression node enables you to ﬁt both linear and logistic regression models to

your data. You can use both continuous and discrete variables as inputs. The node

supports the stepwise, forward, and backward-selection methods. A point-and-click

interaction builder enables you to create higher-order modeling terms.

The Tree node enables you to perform multiway splitting of your database, based on

nominal, ordinal, and continuous variables. This is the SAS implementation of decision

trees, which represents a hybrid of the best of CHAID, CART, and C4.5 algorithms. The

node supports both automatic and interactive training. When you run the Tree node in

automatic mode, it automatically ranks the input variables by the strength of their

contribution to the tree. This ranking can be used to select variables for use in

subsequent modeling. In addition, dummy variables can be generated for use in

subsequent modeling. Using interactive training, you can override any automatic step

by deﬁning a splitting rule or by pruning a node or subtree.

The Neural Network node enables you to construct, train, and validate multilayer

feed-forward neural networks. By default, the Neural Network node automatically

constructs a multilayer feed-forward network that has one hidden layer consisting of

three neurons. In general, each input is fully connected to the ﬁrst hidden layer, each

12 Overview of the Nodes Chapter 1

hidden layer is fully connected to the next hidden layer, and the last hidden layer is

fully connected to the output. The Neural Network node supports many variations of

this general form.

The Princomp/Dmneural node enables you to ﬁt an additive nonlinear model that uses

the bucketed principal components as inputs to predict a binary or an interval target

variable. The node also performs a principal components analysis and passes the

principal components to the successor nodes.

The User Deﬁned Model node enables you to generate assessment statistics by using

predicted values from a model that you built with the SAS Code node (for example, a

logistic model that uses the SAS/STAT LOGISTIC procedure) or the Variable Selection

node. You can also generate assessment statistics for models that are built by a

third-party software product when you create a SAS data set that contains the

predicted values from the model. The predicted values can also be saved to a SAS data

set and then imported into the process ﬂow with the Input Data Source node.

The Ensemble node creates a new model by averaging the posterior probabilities (for

class targets) or the predicted values (for interval targets) from multiple models. The

new model is then used to score new data. One common approach is to resample the

training data and ﬁt a separate model for each sample. The Ensemble node then

integrates the component models to form a potentially stronger solution. Another

common approach is to use multiple modeling methods, such as a neural network and a

decision tree, to obtain separate models from the same training data set.

The Ensemble node integrates the component models from the complementary

modeling methods to form the ﬁnal model solution. The Ensemble node can also be

used to combine the scoring code from stratiﬁed models. The modeling nodes generate

different scoring formulas when they operate on a stratiﬁcation variable (for example, a

group variable such as Gender) that you deﬁne in a Group Processing node. The

Ensemble node combines the scoring code into a single DATA step by logically dividing

the data into blocks by using IF-THEN DO/END statements.

It is important to note that the ensemble model that is created from either approach

can be more accurate than the individual models only if the individual models differ.

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