SAS企业矿工数据挖掘实战：第二版

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"data mining using sas enterprise miner - A Case Study Approach, Second Edition" 《数据挖掘使用SAS企业矿工：案例研究方法》第二版是一本详细介绍如何利用SAS Enterprise Miner进行数据挖掘的专业书籍。该书由SAS Institute Inc.于2003年出版，旨在帮助读者通过实际案例深入理解数据挖掘技术及其在SAS软件中的应用。 SAS Enterprise Miner是SAS公司提供的一款强大的数据挖掘工具，它允许用户通过图形化的用户界面来构建、测试和部署预测模型。这本书以案例研究的方式，使读者能够逐步了解并掌握数据挖掘过程，包括数据预处理、特征选择、模型构建、验证和优化等关键步骤。在数据挖掘过程中，SAS Enterprise Miner支持多种算法，如决策树、神经网络、支持向量机、贝叶斯网络以及聚类分析等。这些算法在书中都会通过具体案例进行详尽解释。读者将学习如何根据业务问题选择合适的模型，并理解不同算法的工作原理和适用场景。此外，书中还强调了模型评估和性能度量的重要性，介绍了如ROC曲线、精确度、召回率、F1分数等评估指标，帮助用户判断模型的预测能力。同时，书中可能涵盖模型管理和部署的内容，讨论如何将构建好的模型应用于实际业务中，以实现数据驱动的决策。对于美国政府用户，该软件及其文档的使用、复制和披露需遵守与SAS Institute签订的协议和FAR52.227-19条款（1987年6月）中的商业计算机软件限制权利规定。 SAS Publishing是SAS公司旗下的出版部门，提供一系列书籍和电子资源，旨在帮助用户充分利用SAS软件，提高其在数据分析、统计建模和数据挖掘领域的技能。这本书的初次印刷是在2003年4月，适合对数据挖掘感兴趣，尤其是希望通过SAS Enterprise Miner进行实践操作的分析师、数据科学家以及相关领域的专业人士阅读。通过《数据挖掘使用SAS企业矿工》，读者不仅能学习到数据挖掘的基础理论，还能获得实际操作SAS Enterprise Miner的宝贵经验，提升在大数据时代解决复杂问题的能力。

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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