├── AssociationAnalysis
├── AssociationDiscovery.pdf
├── AssociationDiscovery.xml
└── README.md
├── Clustering
├── ClusterNodeExplore.pdf
├── ClusterNodeExplore.xml
├── ClusterNodePredict.pdf
├── ClusterNodePredict.xml
└── README.md
├── ContributorAgreement.txt
├── CreditScoring
├── README.md
├── Reject Inference.pdf
├── RejectInference.xml
├── ReverseScorecard.pdf
└── ReverseScorecard.xml
├── EnsembleModeling
├── EnsembleFullFlow.xml
├── EnsembleModeling.pdf
├── EnsembleSubflow.xml
└── README.md
├── LICENSE.txt
├── MARS
├── MARS.xml
├── MARS_16.gif
├── MARS_32.gif
├── README.md
├── README.pdf
└── emextn.sas7bcat
├── PredictiveModeling
├── HPPredictiveModeling.pdf
├── HPPredictiveModeling.xml
├── HPPredictiveModelingTip.pdf
├── HPPredictiveModelingTip.xml
├── PredictiveModeling.pdf
├── PredictiveModeling.xml
└── README.md
├── README.md
├── README_imgs
├── AssociationDiscovery.png
├── ClusterNodeExplore.png
├── ClusterNodePredict.png
├── EnsemblePMSubflow.PNG
├── EnsembleSubflow.PNG
├── HPPredictiveModeling.png
├── HPPredictiveModelingTip.png
├── PredictiveModeling.png
├── RejectInference.png
├── ReverseScorecard.png
├── Survival.png
├── SurvivalTVC.png
├── TextMiningClassify.png
├── TextMiningExplore.png
└── TimeSeriesExplore.png
├── SUPPORT.md
├── SurvivalAnalysis
├── README.md
├── Survival.pdf
├── Survival.xml
├── SurvivalTVC.pdf
└── SurvivalTVC.xml
├── TextMining
├── README.md
├── TextMiningClassify.xml
├── TextMiningExplore.xml
├── TextMining_Classify.pdf
└── TextMining_Explore.pdf
└── TimeSeries
├── README.md
├── TimeSeriesExplore.pdf
└── TimeSeriesExplore.xml
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1 | ## Association Analysis
2 |
3 | ##### Example 1: Association Discovery Using SAS® Enterprise Miner™
4 |
5 | 
6 |
7 | ###### Goal:
8 | The goal is to identify the association between different actions by creating rules. These
9 | rules will then be used to make recommendations (to predict future actions) for each customer.
10 |
11 | ###### Files:
12 | AssociationDiscovery.xml, AssociationDiscovery.pdf
13 |
14 | ***
15 |
16 | License:
17 |
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/Clustering/README.md:
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1 | ## Clustering
2 |
3 | ##### Example 1: Using the Cluster and Segment Profile Nodes in SAS® Enterprise Miner™
4 |
5 | 
6 |
7 | ###### Goal:
8 | The goal is to use the Cluster node and the Segment Profile node to explore the data.
9 |
10 | ###### Files:
11 | ClusterNodeExplore.xml, ClusterNodeExplore.pdf
12 |
13 | ***
14 |
15 | ##### Example 2: Using the Cluster Node in a Predictive Model in SAS® Enterprise Miner™
16 |
17 | 
18 |
19 | ###### Goal:
20 | The goal is to use the Cluster node to create segments that are used as input to a
21 | predictive model and to compare this model with a similar model that does not have this input.
22 |
23 | ###### Files:
24 | ClusterNodePredict.xml, ClusterNodePredict.pdf
25 |
26 | ***
27 |
28 | License:
29 |
--------------------------------------------------------------------------------
/ContributorAgreement.txt:
--------------------------------------------------------------------------------
1 | Contributor Agreement
2 |
3 | Version 1.1
4 |
5 | Contributions to this software are accepted only when they are
6 | properly accompanied by a Contributor Agreement. The Contributor
7 | Agreement for this software is the Developer's Certificate of Origin
8 | 1.1 (DCO) as provided with and required for accepting contributions
9 | to the Linux kernel.
10 |
11 | In each contribution proposed to be included in this software, the
12 | developer must include a "sign-off" that denotes consent to the
13 | terms of the Developer's Certificate of Origin. The sign-off is
14 | a line of text in the description that accompanies the change,
15 | certifying that you have the right to provide the contribution
16 | to be included. For changes provided in source code control (for
17 | example, via a Git pull request) the sign-off must be included in
18 | the commit message in source code control. For changes provided
19 | in email or issue tracking, the sign-off must be included in the
20 | email or the issue, and the sign-off will be incorporated into the
21 | permanent commit message if the contribution is accepted into the
22 | official source code.
23 |
24 | If you can certify the below:
25 |
26 | Developer's Certificate of Origin 1.1
27 |
28 | By making a contribution to this project, I certify that:
29 |
30 | (a) The contribution was created in whole or in part by me and I
31 | have the right to submit it under the open source license
32 | indicated in the file; or
33 |
34 | (b) The contribution is based upon previous work that, to the best
35 | of my knowledge, is covered under an appropriate open source
36 | license and I have the right under that license to submit that
37 | work with modifications, whether created in whole or in part
38 | by me, under the same open source license (unless I am
39 | permitted to submit under a different license), as indicated
40 | in the file; or
41 |
42 | (c) The contribution was provided directly to me by some other
43 | person who certified (a), (b) or (c) and I have not modified
44 | it.
45 |
46 | (d) I understand and agree that this project and the contribution
47 | are public and that a record of the contribution (including all
48 | personal information I submit with it, including my sign-off) is
49 | maintained indefinitely and may be redistributed consistent with
50 | this project or the open source license(s) involved.
51 |
52 | then you just add a line saying
53 |
54 | Signed-off-by: Random J Developer
55 |
56 | using your real name (sorry, no pseudonyms or anonymous contributions.)
--------------------------------------------------------------------------------
/CreditScoring/README.md:
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1 | ## Credit Scoring
2 |
3 | ##### Example 1: Scorecard and Reverse Scorecard Using Credit Scoring for SAS® Enterprise Miner™
4 |
5 | 
6 |
7 | ###### Goal:
8 | Scorecards are the standard model for credit scoring because they are easy to interpret and their output can be easily used to
9 | score new applications. This flow diagram shows the basic steps to build a scorecard and a reverse scorecard. In the output of a
10 | scorecard, the higher the score, the less likely a client is to default. On a reverse scorecard, the higher the score,
11 | the more likely a client is to default.
12 |
13 | ###### Files:
14 | ReverseScorecard.xml, ReverseScorecard.pdf
15 |
16 | ***
17 |
18 | ##### Example 2: Reject Inference Using Credit Scoring for SAS® Enterprise Miner™
19 |
20 | 
21 |
22 | ###### Goal:
23 | A scorecard that is developed using only the accepted applicants may incur sample bias. The goal of a reject inference
24 | diagram flow is to solve the bias by calibrating the scorecard in context with a population that includes both accepted and
25 | rejected observations. This population is usually known as the through-the-door population.
26 |
27 | ###### Files:
28 | RejectInference.xml, RejectInference.pdf
29 |
30 | ***
31 |
32 | License:
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1 | ## Ensemble Modeling
2 | Please see the SAS Global Forum 16 paper [Ensemble Modeling: Recent Advances and Applications](http://support.sas.com/resources/papers/proceedings16/SAS3120-2016.pdf) by Wendy Czika, Miguel Maldonado, and Ye Liu for details on the flows provided here that implement ensemble methods that take model performance into account.
3 |
4 | ***
5 |
6 | ##### Example 1: Full Flow for Ensemble Modeling Using SAS® Enterprise Miner™
7 |
8 | 
9 |
10 |
11 | ###### Goal:
12 | The goal is to run an entire flow to perform predictive modeling and apply the ensemble methods from the SAS Global Forum paper. The full flow consists of the predictive modeling portion of the flow shown here (the "Common Practices" flow from the SAS Global Forum paper) connected to the ensemble subflow shown below in Example 2.
13 |
14 | ###### File: EnsembleFullFlow.xml
15 |
16 | ***
17 |
18 | ##### Example 2: Ensemble Subflow for Ensemble Modeling Using SAS® Enterprise Miner™
19 |
20 | 
21 |
22 | ###### Goal:
23 | The goal is to apply ensemble methods from the SAS Global Forum paper to an existing predictive modeling flow. After importing the XML, you can copy this subflow into the diagram with your predictive modeling flow and connect them together by attaching your model nodes to the leftmost Control Point node in this subflow.
24 |
25 |
26 | ###### File: EnsembleSubflow.xml
27 |
28 | ***
29 |
30 |
31 |
32 |
33 |
34 | License:
35 |
--------------------------------------------------------------------------------
/LICENSE.txt:
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1 | Multivariate Adaptive Regression Splines (Friedman, 1991) is a nonparametric technique that combines regression splines and model selection methods. It is a powerful predictive modeling tool because 1) it extends linear models to analyze nonlinear dependencies 2) it produces parsimonious models that do not overfit the data and thus have good predictive power. Multivariate adaptive regression splines construct spline basis functions in an adaptive way by automatically selecting appropriate knot values for different variables. This can help E-miners to identify linear and nonlinear variables, and the interactions of them as well. When excluding higher order terms, multivariate adaptive regression splines are really good at identifying the effects of single variables in a multivariate setting. This makes it highly usable in process control and for identifying experimental designs. Multivariate adaptive regression splines also has its application in forecasting as a variable screening tool.
2 |
3 | It has always been a desirable tool for our E-miners and now you have multivariate adaptive regression splines as an extension node in Enterprise Miner by just following a few simple steps.
4 |
5 | 1. Download all the files from this Github repository, including a XML file (MARS.xml) defining the node properties, a SAS catalog (emextn.sas7bcat), and two GIF files (MARS_16.gif and MARS_32.gif) for the node icon.
6 |
7 | 2. To deploy the extension node, you need to follow the steps as instructed in Chapter 5 “Deploying an Extension Node” in [“SAS® Enterprise Miner™ 14.1 Extension Nodes: Developer’s Guide”](http://support.sas.com/documentation/cdl/en/emxndg/67980/PDF/default/emxndg.pdf).
8 |
9 | 3. After storing the files in the proper directories, restart the Enterprise Miner server if necessary.
10 |
11 | 4. The MARS extension node runs with SAS Enterprise Miner 13.1 or any later version.
12 |
13 | Please see this [SAS Data Mining and Machine Learning Community tip](https://communities.sas.com/t5/SAS-Communities-Library/Tip-Fit-Multivariate-Adaptive-Regression-Splines-in-SAS/ta-p/328133) for more details.
14 |
15 |
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1 | ## Predictive Modeling
2 |
3 | ##### Example 1: Predictive Modeling in SAS® Enterprise Miner™
4 |
5 | 
6 |
7 | ###### Goal:
8 | The goal is to create a model to predict which home loans will be bad loans (that is, will be defaulted on).
9 |
10 | ###### Files:
11 | PredictiveModeling.xml, PredictiveModeling.pdf
12 |
13 | ***
14 |
15 | ##### Example 2: High-Performance Predictive Modeling Using SAS® Enterprise Miner™
16 |
17 | 
18 |
19 | ###### Goal:
20 | The goal is to create a model for a binary target that predicts which home loans are likely to be defaulted. This flow
21 | uses high-performance nodes in SAS Enterprise Miner. If you set up a grid environment, the high-performance nodes run in a
22 | distributed computing environment, enabling you to use a large number of observations and variables to train and assess a
23 | predictive in a shorter period of time. These high-performance nodes are built on multithreaded procedures, so you might see
24 | performance gain even when you run these nodes on single-machine mode.
25 |
26 | ###### Files:
27 | HPPredictiveModeling.xml, HPPredictiveModeling.pdf
28 |
29 | ***
30 |
31 | ##### Example 3: SAS® High-Performance Analytics tip #3: Example flow diagram in SAS® Enterprise Miner™
32 |
33 | 
34 |
35 | ###### Goal:
36 | Read about this example on SAS communities site at:
37 |
38 | ###### Files:
39 | HPPredictiveModelingTip.xml, HPPredictiveModelingTip.pdf
40 |
41 | ***
42 |
43 | License:
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/README.md:
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1 | # SAS Enterprise Miner Data Mining Examples
2 |
3 | **Archived project: This project is no longer under active development and was archived on 2024-07-22.**
4 |
5 | ## Overview
6 | This repository contains example diagrams and materials for using SAS Enterprise Miner to perform data mining.
7 |
8 | The repository includes XML files (which represent SAS Enterprise Miner process flow diagrams) for association analysis,
9 | clustering, credit scoring, ensemble modeling, predictive modeling, survival analysis, text mining, time series, and accompanying PDF files to help guide you through the process flow diagrams.
10 |
11 | - [/AssociationAnalysis](https://github.com/sassoftware/dm-flow/tree/master/AssociationAnalysis) contains XML and PDF files about running an example for Association Analysis.
12 | - [/Clustering](https://github.com/sassoftware/dm-flow/tree/master/Clustering) contains XML and PDF files about running an example for Clustering.
13 | - [/CreditScoring](https://github.com/sassoftware/dm-flow/tree/master/CreditScoring) contains XML and PDF files about running an example for Credit Scoring.
14 | - [/EnsembleModeling](https://github.com/sassoftware/dm-flow/tree/master/EnsembleModeling) contains XML and PDF files about running an example for Ensemble Modeling.
15 | - [/MARS](https://github.com/sassoftware/dm-flow/tree/master/MARS) contains XML and PDF files about running an example for Multivariate Adaptive Regression Splines (MARS).
16 | - [/PredictiveModeling](https://github.com/sassoftware/dm-flow/tree/master/PredictiveModeling) contains XML and PDF files about running an example for Predictive Modeling.
17 | - [/SurvivalAnalysis](https://github.com/sassoftware/dm-flow/tree/master/SurvivalAnalysis) contains XML and PDF files about running an example for Survival Analysis.
18 | - [/TextMining](https://github.com/sassoftware/dm-flow/tree/master/TextMining) contains XML and PDF files about running an example for Text Mining.
19 | - [/TimeSeries](https://github.com/sassoftware/dm-flow/tree/master/TimeSeries) contains XML and PDF files about running an example for Time Series.
20 |
21 | # Prerequisites
22 | ## System Requirements
23 | These examples were tested in the following environment:
24 | - Windows Server 2008 R2 Enterprise
25 | - Dual Intel Xeon E5-2667 @ 2.9 GHz 128 GB RAM
26 | - SAS 9.4 (TS1M2)
27 | - SAS Enterprise Miner 13.2
28 |
29 |
30 |
31 | # Getting Started
32 | Download (and unzip) or clone this repository. The repository contains one directory for each data mining topic
33 | (clustering, survival analysis, and so on). Each directory contains one or more example XML files (diagrams)
34 | and associated PDF documentation.
35 |
36 | To run these examples:
37 |
38 | 1. Create a new Project or open an existing project in SAS Enterprise Miner.
39 |
40 | 2. Right-click on the Diagrams folder in the top left corner, and select "Import Diagram from XML." Select the
41 | XML file from one of the directories, and open the corresponding PDF document to learn about the technique
42 | implemented in the example.
43 |
44 | NOTE: You can import multiple XML files into the same SAS Enterprise Miner project or you can choose to
45 | create a separate project for every topic.
46 |
47 | 3. Right-click on the last node and select "Run" to run the process flow diagram.
48 |
49 |
50 | # Contributors
51 |
52 | Ralph Abbey, Wendy Czika, Funda Gunes, Susan Haller, Miguel Maldonado and Radhikha Myneni
53 |
54 | # Contributing
55 | The [Contributor Agreement](https://github.com/sassoftware/dm-flow/blob/master/ContributorAgreement.txt) details on how to make contributions on this project.
56 |
57 | # License
58 | Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at [LICENSE.txt](https://github.com/sassoftware/dm-flow/blob/master/LICENSE.txt)
59 |
60 | Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
61 |
62 |
63 |
64 | Copyright SAS Institute.
65 |
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/README_imgs/TimeSeriesExplore.png:
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/SUPPORT.md:
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1 | ## Support
2 |
3 | We use GitHub for tracking bugs and feature requests. Please submit a GitHub issue or pull request for support.
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/SurvivalAnalysis/README.md:
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1 | ## Survival Analysis
2 |
3 | ##### Example 1: Survival Analysis Using SAS® Enterprise Miner™
4 |
5 | 
6 |
7 | ###### Goal:
8 | The goal is to estimate when customers will cancel their subscription (churn). Estimating when customers will cancel
9 | subscriptions (as opposed to predicting which customers will cancel or whether they will cancel) enables you to view
10 | trends in time about churn and make decisions accordingly. Survival analysis can also be used to model other types of
11 | events or failures (for example, when objects will break or become unusable). This process flow diagram examines the
12 | use of the Survival node without the use of time-varying covariates.
13 |
14 | ###### Files:
15 | Survival.xml, Survival.pdf
16 |
17 | ***
18 |
19 | ##### Example 2: Survival Analysis with Time-Varying Covariates Using SAS® Enterprise Miner™
20 |
21 | 
22 |
23 | ###### Goal:
24 | The goal is to determine when a customer is most likely to upgrade. Estimating when customers upgrade (as
25 | opposed to whether they upgrade) enables you to view upgrade trends in time and make decisions accordingly. This example
26 | examines the use of the Survival node with time-varying covariates incorporated to potentially improve the model.
27 |
28 | ###### Files:
29 | SurvivalTVC.xml, SurvivalTVC.pdf
30 |
31 | ***
32 |
33 | License:
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/SurvivalAnalysis/Survival.pdf:
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/SurvivalAnalysis/Survival.xml:
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/SurvivalAnalysis/SurvivalTVC.pdf:
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/SurvivalAnalysis/SurvivalTVC.xml:
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/TextMining/README.md:
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https://raw.githubusercontent.com/sassoftware/dm-flow/2f4c346103bbadc91e52d2b64e91fed83ae4375d/TextMining/README.md
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/TextMining/TextMining_Classify.pdf:
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/TextMining/TextMining_Explore.pdf:
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/TimeSeries/README.md:
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1 | ## Time Series
2 |
3 | ##### Example 1: Time Series Exploration and Comparison Using SAS® Enterprise Miner™
4 |
5 | 
6 |
7 | ###### Goal:
8 | The goal is to explore a time series data set and organize it into a format for further analysis that compares
9 | different time series in the data.
10 |
11 | ###### Files:
12 | TimeSeriesExplore.xml, TimeSeriesExplore.pdf
13 |
14 | ***
15 |
16 | License:
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/TimeSeries/TimeSeriesExplore.pdf:
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/TimeSeries/TimeSeriesExplore.xml:
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