Module 17: Introduction to Data Mining

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Module 17: Introduction to Data Mining

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  1. Module 17: Introduction to Data Mining Contents Overview 1 Introducing Data Mining 2 Training a Data Mining Model 12 Building a Data Mining Model with OLAP Data 13 Browsing the Dependency Network 23 Lab A: Creating a Decision Tree with Relational Data 27 Review 32
  2. Information in this document is subject to change without notice. The names of companies, products, people, characters, and/or data mentioned herein are fictitious and are in no way intended to represent any real individual, company, product, or event, unless otherwise noted. Complying with all applicable copyright laws is the responsibility of the user. No part of this document may be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without the express written permission of Microsoft Corporation. If, however, your only means of access is electronic, permission to print one copy is hereby granted. Microsoft may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in this document. Except as expressly provided in any written license agreement from Microsoft, the furnishing of this document does not give you any license to these patents, trademarks, copyrights, or other intellectual property.  2000 Microsoft Corporation. All rights reserved. Microsoft, BackOffice, MS-DOS, Windows, Windows NT, are either registered trademarks or trademarks of Microsoft Corporation in the U.S.A. and/or other countries. The names of companies, products, people, characters, and/or data mentioned herein are fictitious and are in no way intended to represent any real individual, company, product, or event, unless otherwise noted. Other product and company names mentioned herein may be the trademarks of their respective owners. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  3. Module 17: Introduction to Data Mining iii Instructor Notes Presentation: This module introduces students to data mining and explains how to build and 40 Minutes browse data mining models by using Microsoft® SQL Server™ 2000 Analysis Services. Students will learn fundamental data mining terminology, concepts, Lab: techniques, and algorithms. 20 Minutes This is an overview module that focuses on the use of built-in Analysis Manager wizards. It is not intended to provide in-depth knowledge of data mining. After completing this module, students will be able to: Describe data mining characteristics, applications, and modeling techniques. ! Describe the process of training a model. ! Use the online analytical processing (OLAP) Mining Model Wizard to edit, ! process, and explore the decision trees. Analyze relational data relationships in the dependency network browser. ! Describe the steps required to build a clustering model by using OLAP data. ! Materials and Preparation This section lists the required materials and preparation tasks that you need to teach this module. Required Materials To teach this module, you need Microsoft PowerPoint® file 2074A_17.ppt. Preparation Tasks To prepare for this module, you should: Read all the materials for this module. ! Read the instructor notes and margin notes. ! Practice combining the lecture with the demonstrations. ! Complete the lab. ! Review the Trainer Preparation presentation for this module on the Trainer ! Materials compact disc. Review any relevant white papers that are located on the Trainer Materials ! compact disc. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  4. iv Module 17: Introduction to Data Mining Demonstration: Determining Why Students Attend College The following demonstration procedures provide information that will not fit in the margin notes or is not appropriate for student notes. Demonstration: 10 Minutes In this demonstration, you will create a data mining model by using a decision tree with relational data. Specifically, you will create a decision tree that determines why students attend college. You will create a new OLAP database with a data source connecting to the Module 17 relational database. ! To create an OLAP database 1. In Analysis Manager, expand the Analysis Servers folder, right-click your local server, and then click New Database. 2. Enter Module 17 as the database name, and then click OK. 3. Expand the Module 17 database, right-click the Data Sources folder, and then click New Data Source. 4. On the Provider tab of the Data Link Properties dialog box, click Microsoft OLE DB Provider for SQL Server. Click Next. 5. Type localhost in Step 1. 6. In Step 2, click Use Windows NT Integrated security. 7. In Step 3, click Module 17 from the list of databases. Click OK. ! To create the data mining model In this procedure, you will create the data mining model by selecting source, case table, data mining technique, and key column. 1. In the Module 17 database, right-click the Mining Models folder, and then click New Mining Model. 2. At the welcome page, click Next. 3. From the Select source type step of the Mining Model Wizard, click Relational data, and then click Next. Point out that either relational tables or OLAP cubes can be used as source data. For this model, you are accessing relational data. 4. From the Select case tables step, in the Available tables list, click College Plans, and then click Next. 5. From the Select data mining technique step, in the Technique list, click Microsoft Decision Trees, and then click Next. Two algorithms ship with Analysis Services: Microsoft Decision Trees and Microsoft Clustering. Use the Decision Trees algorithm for this demonstration. 6. From the Select the key column step, in the Case key column list, click StudentID, and then click Next. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  5. Module 17: Introduction to Data Mining v ! To select input and predictable columns for the mining model 1. From the Select input and predictable columns step of the Mining Model Wizard, in the Available columns list, click CollegePlans at the bottom of the column list. 2. Click the top arrow (>) to choose CollegePlans as a predictable column. 3. In the Available columns list, click Gender, and then click the bottom arrow (>) to choose that column as an input column. 4. In the Available columns list, click ParentIncome, and then click the bottom arrow (>) to choose that column as an input column. 5. In the Available columns list, click IQ, and then click the bottom arrow (>) to select that column as an input column. 6. In the Available columns list, click ParentEncouragement, and then click the bottom arrow (>) to select that column as an input column. Click Next. ! To finish the Mining Model Wizard In this procedure, you name the model, initiate processing and then close the wizard. 1. From the Finish the mining model wizard step, in the Model name box, type CollegePlans. 2. Click Finish to create and process the model. 3. When the model has completed processing, click Close to close the Process dialog box. ! To explore data in the decision tree 1. In the Relational Mining Model Editor, click the Content tab. 2. In the Content Detail pane, click the All node. View the Totals tab of the Attributes pane, and point out that more than 67 percent of the students interviewed do not plan to attend college. 3. Click the Parent Encouragement = Encouraged node. Point out to the students that parental encouragement is the most dominant attribute in this model. More than 57 percent of students that are encouraged by their parents plan to attend college. 4. Click Parent Encouragement = Not Encouraged. Fewer than 7 percent of students who are not encouraged by their parents plan to attend college. 5. Close the Relational Mining Model Editor. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  6. vi Module 17: Introduction to Data Mining Module Strategy Use the following strategy to present this module: The structure of this module is multiple demonstrations showing students how to build and browse various types of data mining models. Except for the first example about students attending college, the demonstrations are documented directly in the student manual. Integrate your lecture with live demonstration following the procedures included in the student notes. Encourage students to follow along with your demonstrations on their computers. Some students may choose to watch your demonstrations only, which is also acceptable. Introducing Data Mining ! The case study introduces students to data mining. Data mining may be new to many students and should be described in very simple terms highlighting the business application and uses. Emphasize to students why this technology is useful and complementary to the other forms of analysis they have been exposed to. Then describe the various data mining techniques that are available. Training a Data Mining Model ! Describe the process required to create a data mining model. Define training data and cases. Building a Data Mining Model with OLAP Data ! Introduce students to the membership card scenario. Use the membership card scenario to step students through the process of building a data mining model with OLAP data by using the Mining Model Wizard. Describe each step in the process—selecting the data mining technique, selecting the case, selecting the training data, creating a dimension and virtual cube, and browsing the data mining model. Browsing the Dependency Network ! Demonstrate how to browse the dependency network. Explain that the Dependency Network Browser can be used to view all the relationships in your model. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  7. Module 17: Introduction to Data Mining 1 Overview Topic Objective To provide an overview of the module topics and Introducing Data Mining objectives. ! Lead-in Training a Data Mining Model ! In this module, you will learn about data mining, how data Building a Data Mining Model with OLAP Data ! mining can be used to Browsing the Dependency Network address business ! application requirements, and how to create data mining models by using the Analysis Manager. This module provides you with an introduction to Microsoft® SQL Server™ 2000 Analysis Services Data Mining. The objective of the module is to introduce you to both data mining principles and applications while exploring the Analysis Services wizard-driven interface for creating data mining models. After completing this module, you will be able to: Describe data mining characteristics, applications, and modeling techniques. ! Describe the process of training a model. ! Use the online analytical processing (OLAP) Mining Model Wizard to edit, ! process, and explore the decision trees. Analyze relational data relationships in the dependency network browser. ! Describe the steps required to build a clustering model by using OLAP data. ! BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  8. 2 Module 17: Introduction to Data Mining # Introducing Data Mining Topic Objective To introduce the concept of data mining. Defining Data Mining ! Lead-in In this section, you will be Data Mining Applications ! introduced to a simple case study example. In that Data Mining Models ! example, data mining will be Introductory Example defined, common ! applications and techniques Exploring the Decision Tree discussed, and its role in the ! data warehouse explored. This section introduces data mining concepts, including: Defining data mining. ! Discussing how data mining can be applied to solve common business ! applications. Describing what data mining models are available. ! Presenting a simple example of how data mining can be used. ! Exploring the decision tree. ! BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  9. Module 17: Introduction to Data Mining 3 Defining Data Mining Topic Objective To provide a definition of data mining. Is The Process of Deducing Meaningful Patterns and ! Lead-in Rules from Large Quantities of Data Data mining provides a means by which the system Searches for Patterns in Data Rather than Answering ! deduces knowledge from Predefined Questions the data by identifying correlations and other Is Used To: ! patterns in the data. Provide historical insights $ Predict future values or outcomes $ Close the loop for analysis $ In many organizations, data volumes are so large that it is difficult, even for the most seasoned analyst, to identify the key information most relevant to managing the business. Data mining is the automatic or semi-automatic process of deducing meaningful patterns and rules from large quantities of data. These patterns provide valuable insights to business managers and offer information that may be overlooked by more traditional manual methods of analysis. Data mining programs search for patterns in data rather than answer predefined questions. Because of this, they can be used for knowledge discovery in addition to hypothesis testing. Data mining is used to: Provide insight into historical data. ! Predict future values or outcomes based on historical patterns. ! Close the analysis loop by taking action based on the information derived ! from the analysis. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  10. 4 Module 17: Introduction to Data Mining Data Mining Applications Topic Objective Advertising on the Internet ! To identify different applications for data mining. “What banner will I display to this visitor?” $ Lead-in “What other products is this customer likely to buy? $ Data mining is used for a variety of different Detecting Fraud ! applications. We are now going to talk about some “Is this insurance claim a fraud?” $ common uses. Pricing Insurance ! “How much of a discount will I offer to this customer?” $ Managing Credit Risk ! “Will I approve the loan for this customer?” $ Data mining techniques are used in a variety of applications. This section Delivery Tips provides some interesting examples. Incorporate your own examples of how data Advertising on the Internet mining is used to solve business problems. Ask You can use data mining to classify groups of customers with similar students for examples from information into segments for targeting advertising or special offers. their businesses. Following are two Internet customer examples: Point out that data mining is no longer an art used by just An e-commerce Web site sells sporting equipment. When a customer ! PhDs. This technology is registers, a database management system collects information about the available and useful to a customer, such as gender, marital status, favorite sport, and age. variety of businesses. By using data mining techniques, the Web site displays a masculine banner ad with a golfing motif for the male, golf-loving, 40-year-old who returns to the Web site after registering. When you purchase merchandise on the Internet, you are sometimes offered ! additional merchandise that the Web site predicts you might be interested in—for example, a book similar to the one you are currently purchasing. Such recommendations are based on data mining techniques that search out purchase patterns of customers who purchased the same book you are now buying. The system recommends: “If you like xyz books, check out the additional books below.” Detecting Fraud You can use a data mining system to identify characteristics of suspicious insurance claims by analyzing characteristics of legitimate and fraudulent claims. For example, specific types of injuries that are difficult to diagnose, such as neck and back injuries, may be more likely candidates for a fraudulent claim. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  11. Module 17: Introduction to Data Mining 5 Pricing Insurance In the insurance industry, you use data mining techniques to analyze historical data such as age, marital status, gender, and driving history. All these factors play a role in predicting the likelihood of a specific driver for getting into an automobile accident. Data mining techniques help you to weigh and factor these data points into pricing for an individual insurance policy. Managing Credit Risk When you apply for a loan, the bank collects a broad range of information about you—for example, income, years of employment at a current job, marital status, and credit standing. By using data mining techniques applied to historical loan application information, the bank can predict whether you are a good or bad credit risk and can use this information when deciding on loan approval. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  12. 6 Module 17: Introduction to Data Mining Data Mining Models Topic Objective To describe different data mining models and how they Analysis Services Models apply to data analysis. ! Lead-in Microsoft Clustering $ A variety of data mining models are available. These Microsoft Decision trees $ techniques represent Other Models different approaches to ! classification and prediction. Market basket analysis (affinity grouping) $ Memory-based reasoning $ Neural networks $ Several data mining techniques are available that you can use to identify the Delivery Tip patterns in large volumes of data. You use different data mining techniques for Do not spend much time different types of applications. In this section, you will learn the most common describing the different data mining techniques and when to apply them. models. Simply discuss that various models are available Analysis Services Models for analysis and that Microsoft provides two of Analysis Services includes two data mining techniques—Microsoft Clustering the models in Analysis and Microsoft Decision Trees. Services. Clustering You use the clustering technique, sometimes called K-nearest neighbor, to group data records that are similar to each other. You often use this common technique as the starting point for market or customer analysis. For example, you may want to segment your market so that you can offer customized programs and pricing to specific customer groups. With clustering, you can segment your customers into groups with similar characteristics. Decision Trees Decision trees are a popular method for both classifying and predicting. By using a series of questions and rules to categorize data cases, you can predict the likelihood of certain types of cases having a specific outcome. For example, insurance companies use a decision tree to predict the likelihood of high claims by analyzing statistical data organized by a set of rules that help predict the likelihood of high claims. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  13. Module 17: Introduction to Data Mining 7 Other Models Analysis Services provides two types of data mining models—clustering and decision trees. However, users may define their own models or use other proprietary data mining algorithms. Common data mining models include market basket analysis, memory-based reasoning, and neural networks. Market Basket Analysis (Affinity Grouping) Market basket analysis, sometimes called affinity grouping, is used for finding groups of items that occur frequently together in a single transaction. For example, customers who buy gin may also purchase tonic water, which is a frequent accompaniment. Customers who buy potato chips frequently buy potato chip dips on the same shopping trip. Understanding when products sell together helps a retail store manage placement of items on shelves to maximize affinity group purchases. Memory-Based Reasoning Memory-based reasoning (MBR) is a directed data mining technique that is used for prediction and classification. MBR analyzes a collection of the known instances of the nearest neighbor and from that information makes predictions about unknown instances. For example, if a patient exhibits a series of symptoms, doctors apply their experience with similar patients to diagnose the current case. The doctors perform their diagnoses by using a form of MBR. Neural Networks Just as a human can learn from experience, so can computers. Neural networks model the neural connections in a human brain and thereby simulate learning. If you assemble data where the input and output factors are both known, the computer can “learn” from those patterns and set up rules and mathematical factors to help calculate or predict the output value. Suppose you want to sell your car. Several factors affect the sales price, such as the age of the car, its condition, its manufacturer and model, and so forth. Analyzing historical car prices, the neural network can create a series of input and output factors to predict the sales price. Summary of Models The following table defines commonly used data mining models and their typical usages. Technique Typical usage Clustering (K-nearest neighbor) Classification Decision trees Classification and prediction Market basket analysis (affinity Clustering or affinity grouping grouping) Memory-based reasoning Classification and prediction Neural networks Classification, prediction, and clustering BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  14. 8 Module 17: Introduction to Data Mining Introductory Example Topic Objective Why Do High School Students Attend College? To introduce an example of how data mining can be used for prediction. Lead-in What do you think is the principal attribute for predicting whether students attend college? What, if anything, can you conclude from the information in the table? A survey was conducted recently in the United States asking high-school Delivery Tips seniors to answer the following five questions: Browse the actual relational table data when discussing 1. What is your gender? the case study. You can find 2. What is your parents' income? the CollegePlans table in the Module 17 SQL Server 3. What is your IQ? 2000 database. 4. Do your parents encourage or not encourage you to go to college? Ask students what they think 5. Do you plan to attend college? are the most dominant attributes that will predict Data from the survey was compiled into a table shown in the preceding whether a student plans to illustration. attend college. Glancing at the table, you cannot easily determine how many students plan to attend college and how many do not. You can see that roughly 50 percent will attend based on the first 22 records of this file. This result may or may not be representative of the whole set of 9,000 cases. To determine how many students plan to attend college, you can execute a query that counts students grouped by those planning on attending and those not planning on attending. Suppose you are interested in determining the attribute or combination of attributes that have the highest potential of predicting the likelihood of a student for attending college. This is a more complex question and involves segmenting the data based on various attributes you collect. To answer the question, you can spend several hours exploring the data manually, or you can use data mining to explore the data automatically. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  15. Module 17: Introduction to Data Mining 9 Demonstration: Determining Why Students Attend College Topic Objective To demonstrate how to create a data mining model by using a decision tree with relational data. Lead-in In this demonstration, you will learn how to create a decision tree that determines what causes students to attend college. In this demonstration, you will create a data mining model by using a decision Delivery Tips tree with relational data. Specifically, you will create a decision tree that The steps for this determines what causes students to attend college. demonstration are included in the Instructor Notes. Encourage students to follow your demonstration on their computers. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  16. 10 Module 17: Introduction to Data Mining Exploring the Decision Tree Topic Objective All Students To demonstrate how data Attend College: mining is applied by using a 33% Yes decision tree. 67% No Lead-in Parental Parents Encourage = Yes Parents Encourage = No Looking at all the students Encouragement ? interviewed, roughly 33 Attend College: Attend College: percent plan to attend and 57% Yes 6% Yes the remaining do not plan to 43% No 94% No attend. High IQ IQ IQ Attend College: 18% Yes Low IQ 82% No Attend College: High IQ Low IQ Medium IQ 4% Yes Attend College: Attend College: Attend College: 96% No 74% Yes 29% Yes 9% Yes 26% No 71% No 91% No Applying a decision tree algorithm to the survey data, the following Delivery Tips relationships are discovered: After switching to the slide, ask students the following Of students surveyed, 32.68 percent plan to attend college. The remaining ! question: Of the collected students do not plan to attend. attributes, which do you The most dominant attribute in predicting whether a student is likely to think is most likely to have ! attend college is whether their parents encourage them to attend. an impact on a student’s decision to attend college? Tip The most dominant attribute is always the first rule in the decision tree. Then use the build slide to step through the results. Students who received encouragement from their parents had a 57.27 ! percent probability of planning to attend. This is much higher than the Switch to Analysis Manager to show the same results in general population. Of the students who were encouraged by their parents: the Relational Mining Model • Those with an IQ higher than 110.25 had more than a 74 percent Editor. probability of attending college. • Those who also had parents with a high income were even more likely to attend college—77 percent. Students who did not receive encouragement had a very low probability, ! 6.22 percent, of planning to attend. Of the students who were not encouraged by their parents: • Those students with a very high IQ had a higher probability than those with a lower IQ. Of students with an IQ higher than 118.25, 17.96 percent plan to attend versus 3.52 percent of students with an IQ lower than 99.25. • Parental income had no impact on the likelihood of planning to attend college if the student were exceptionally smart with an IQ higher than 118.25. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  17. Module 17: Introduction to Data Mining 11 This example demonstrates that data mining allows you to validate or discredit specific hypothesis. Data mining also helps you identify patterns that you may not expect or notice by analyzing the data manually. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  18. 12 Module 17: Introduction to Data Mining Training a Data Mining Model Topic Objective Data To explain the methodology To Predict Training Data Mining Model for creating a mining model and to define terminology. Lead-in When creating a data mining model, you need a training data set. This is typically historical data DM DM where the attributes to be Engine Engine predicted are known. Mining Model Predicted Data To create a model, you must assemble a set of data where the attributes to be Delivery Tip predicted are known. Such a data set is called the training data. During the Use the build slide to training process, data is inserted into the data mining model. The data mining explain how Analysis Server model analyzes the training data and looks for rules and patterns that can be evaluates training data to used later to determine the predictive columns. build a data mining model, and then uses the model to You perform training by processing the data mining model in Analysis predict future outcomes Manager. based on new data sets. The training data has two characteristics: It is typically historical data. ! It is statistically representative of the cases for which you are building a ! predictive model. The case is the basic unit for analysis in the mining model. The case is the element that is used for classifying and grouping the data. As depicted in the preceding illustration, the data mining engine evaluates the cases identified in the training data and creates the model based on the algorithm selected. When the model is built, it can be applied to future data to predict outcomes or classify data. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  19. Module 17: Introduction to Data Mining 13 # Building a Data Mining Model with OLAP Data Topic Objective To describe the steps used Introducing the Membership Card Scenario to build a data mining model ! with OLAP data. Selecting the Data Mining Technique ! Lead-in Selecting the Case These are a variety of steps ! involved in building a data Selecting Predicted Entity mining model with OLAP ! data. Selecting Training Data ! Creating a Dimension and Virtual Cube ! Browsing the Data Mining Model ! You can use the Mining Model Wizard in Analysis Manager to create a data mining model. This section uses the Membership Card scenario to demonstrate the creation of a data mining model. Building and reviewing a data mining model entails several steps: 1. Selecting the data mining technique. 2. Identifying the case. 3. Selecting the entity to be predicted. 4. Identifying the training data. 5. Optionally creating a dimension and virtual cube from the resulting model. 6. Processing the model and browsing the results. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
  20. 14 Module 17: Introduction to Data Mining Introducing the Membership Card Scenario Topic Objective The VP of Marketing Wants to Evaluate Member Card Programs To introduce a business ! scenario for creating a Identify opportunities for enhancing services at each current $ model. card level Lead-in Market programs based on customer demographics The Vice President of $ Marketing of Foodmart We Will Predict Card Selection Patterns By Using Data Mining ! wants to evaluate the current member card Find membership card selection patterns $ programs. Select Customer as the mined dimension $ Select the Member Card property as the pattern identifier $ Use Customer demographics to train the model $ Browse the decision tree $ The Vice President of Marketing of Foodmart wants to evaluate current Delivery Tip member card programs. To improve customer retention and satisfaction, she Use this example to specifically wants to identify opportunities for enhancing services provided at describe each of the each card level: following pages in this section. Golden ! Silver ! Bronze ! Normal ! Demographic information about customers is available. The information includes: Gender ! Marital status ! Yearly income ! Education level ! In this card membership scenario, you will learn how historical data in the Foodmart 2000 Sales cube predicts the likelihood of customers applying for different levels of membership cards based on a variety of attributes. BETA MATERIALS FOR MICROSOFT CERTIFIED TRAINER PREPARATION PURPOSES ONLY
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