1. Introduction to Clementine Tutors: Cecia Chan & Gabriel Fung Data Mining Tutorial

2. A Brief Review of Data Mining (I) Data mining is… Data mining is… –A process of extracting previously unknown, valid and actionable knowledge from large databases A rule of thumb: A rule of thumb: –If we know clearly the shape and likely content of what we are looking for, we are probably not dealing with data mining

3. A Brief Review of Data Mining (II) Therefore, data mining is not… Therefore, data mining is not… –SQL queries against any number of disparate database or data warehouse –SQL queries in a parallel or massively parallel environment –Information retrieval, for example, through intelligent agents –Multidimensional database analysis (MDA) –OLAP –Exploratory data analysis (EDA) –Graphical visualization –Traditional statistical processing against a data warehouse However, they are all related to data mining However, they are all related to data mining

4. Data Mining Process 1. Business objective(s) determination –What is your goal? 2. Data collection –You can learn nothing without data… 3. Data preprocessing (or Data preparation) –Remove outlier / filter noise / modify fields / etc 4. Modeling –The core part of data mining 5. Evaluation –See what you have learn!

5. Data Mining Software Existing Data mining software: Existing Data mining software: –Clementine from SPSS (we have this software), Enterprise Minter from SAS (we have this software), Intelligence Miner from IBM (we have this software), MineSet from Silicon Graphics, K-wiz from Compression Sciences Ltd., DBMiner from DBMiner Tech. Inc., PolyAnalyst from Megaputer Intelligence, StatServer from Mathsoft : :

6. Problem Statement Situation: Situation: –You are a researcher compiling data for a medical study –You have collected data about a set of patients, all of whom suffered from the same illness –Each patient responded to one of five drug treatments

7. Step 1: Business objective Figure out which drug might be appropriate for a future patient with the same illness Figure out which drug might be appropriate for a future patient with the same illness Here are the data collected: Here are the data collected: –Age –Sex (M or F) –BP (Blood pressure: High, normal, or low) –Weight (The weight of the patient) –Cholesterol (Blood cholesterol: Normal or high) –Na (Blood sodium concentration) –K (Blood potassium concentration) –Drug (Drug to which the patient responded)

8. Using Clementine (1) Clementine is located in… Clementine is located in… –Start  All Programs  Clementine 6.0.2 Models Nodes Work-Space

9. Using Clementine (2) Nodes in the workspace represent different objects and actions. You connect the nodes to form streams, which, when executed, let you visualize relationships and draw conclusions. Nodes in the workspace represent different objects and actions. You connect the nodes to form streams, which, when executed, let you visualize relationships and draw conclusions.

10. Step 2: Data Collection (1) Double Click Nodes for inputting the collected data

11. Data Collection (2) Location of your file Use how many columns from the file Is the first row specify the names of the fields or not Other details

12. Step 3: Data Preparation – Explore the Data (1) Nodes for exploration/visualization: Nodes for exploration/visualization: –Table (in the Output panel) –Plot (in the Graphs Panel) –Histogram (in the Graphs Panel) –Distribution (in the Graphs Panel) –Web (in the Graphs Panel)

13. Step 3: Data Preparation – Explore the Data (2) Note: Connect the nodes by click-and-drag the middle button of the mouse Double Click Connect the nodes: Connect the nodes:

14. Step 3: Data Preparation – Explore the Data (3) Execution: Execution: Note: Right click on the table node to display this menu

15. Step 3: Data Preparation – Explore the Data (4) Other nodes (Please try the other nodes yourself): Other nodes (Please try the other nodes yourself): –Histogram:

16. Step 3: Data Preparation – Modify the Data (1) Replacing values: Replacing values: –Use Filler node: »Suppose we want to transform all weights to its log value (Note: we usually only transform variables to log when it is highly skewed):

17. Step 3: Data Preparation – Modify the Data (2) Derive a new value: Derive a new value: –Use Derive node: »Suppose we want to combine Na and K:

18. Step 3: Data Preparation – Modify the Data (3) Remove some fields Remove some fields –Use Filter node »Suppose we have derived a new field Na_Over_K, now we need to remove the field Na and K:

19. Step 4: Modeling – Define fields Define the fields Define the fields –Use Type node:

20. Step 4: Modeling – Build a Model (1) It is the core part of data mining. It is the core part of data mining. Supervised Learning: Supervised Learning: –Train Net (Neural Network) –C5.0 (C5.0 Decision Tree) –Linear Reg. (Linear regression) –C & R Tree (Classification and Regression Tree, CART) Unsupervised Learning: Unsupervised Learning: –Train Kohonen (Self-Organized Map, SOM) –Train KMeans (K-means Clustering) –TwoStep (A kind of Hierarchical Clustering) Others: Others: –GRI (Association Rule mining) –Apriori (Association Rule mining) –Factor / PCA (Factor analysis, attribute selection technique)

21. Step 4: Modeling – Build a Model (2) Build what model? Build what model? –Recall that our objective is to determine which type of drugs is suitable for a specific patient. –Thus, it is a classification problem (supervised learning) In this tutorial, we use: In this tutorial, we use: –C5.0 and C & R Tree

22. Step 4: Modeling – Build a Model (3) Note: Note: –There are many complex settings for each model –In this tutorial, we use default setting –Fine tuning a model requires solid experiences in data mining

23. Step 5: Evaluation (1) It means NOTHING even if we have learned SOMETHING, until the knowledge that we have learned are ACTIONABLE and VALID It means NOTHING even if we have learned SOMETHING, until the knowledge that we have learned are ACTIONABLE and VALID Remember: Remember: –The data set of training and testing are ALWAYS different (why?)

24. Step 5: Evaluation (2) Create the following flow Create the following flow Note: Must have the same flow as the training stage

25. Step 5: Evaluation (3) Different results: Different results: –Different models can yield a completely different results –Choosing and tuning a good model is a difficult job –In this tutorial, we only introduce the process of data mining only

26. Assignment 1

27. Assignment 1 – Problem Statement Situation: Situation: –You are a financial analyst of a bank –You have to predict whether a customer is Good or Bad based on some demographic information Data Set: Data Set: –A data set about your past customers has been collected –Each customer is either Good or Bad

28. Assignment 1 – Field definitions VARIABLEROLEDEFINITIONDESCRIPTION CHECKINGinputNominalChecking account status HISTORYinputNominalCredit history AMOUNTinputIntervalAmount in Bank SAVINGSinputNominalNo. of Savings (bonds, stocks, etc) EMPLOYEDinputNominalEmployment Type (Gov., private, etc) INSTALLPinputNominalType of installment rate MARITALinputNominalMartial status PROPERTYinputNominalType of Property AGEinputIntervalAge in years OTHERinputNominalType of other installment plan HOUSINGinputNominalType of House EXISTCRinputIntervalNumber of existing credits JOBinputNominalJob Nature FOREIGNinputBinaryForeign worker or Local worker GOOD_BADOutputBinaryGood or bad credit rating

29. Assignment 1 – Data Mining Process Data Collection Data Collection –Please download CreditRisk data set from http://www.se.cuhk.edu.hk/~ect7470/ http://www.se.cuhk.edu.hk/~ect7470/ –Two data sets: (i) creditRisk1.csv is for training (ii) creditRisk2.csv is for testing Data Preprocessing Data Preprocessing –Please explore the data and think critically whether any data preprocessing is necessary »Hints: Two of the interval variables are highly skewed

30. Assignment 1 – Data Mining Process Modeling Modeling –Please build a prediction models using default settings: »C5.0 Decision Tree Model Assessment Model Assessment –Please use the testing data set to evaluate the performance of the prediction models

31. Assignment 1 –Submission Save the stream as “id.str” Save the stream as “id.str” –E.g, 00123456.str Upload your stream to the course account Upload your stream to the course account Deadline: Deadline: –4 April 2004 This is an individual assignment This is an individual assignment Note: We strongly encourage you to submit this assignment during the class!!! Note: We strongly encourage you to submit this assignment during the class!!!