《数据挖掘导论 Introduction to Data Mining》课程教学资源（PPT课件讲稿）Data Mining Classification（Basic Concepts, Decision Trees, and Model Evaluation）

Data Mining Classification: Basic Concepts, Decision Trees, and model evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar C Tan, Steinbach, Kumar Introduction to Data Mining 18/2004

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1

Classification: definition Given a collection of records(training set Each record contains a set of attributes, one of the attributes is the class Find a mode/ for class attribute as a function of the values of other attributes Goal: previously unseen records should be assigned a class as accurately as possible a test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it C Tan, Steinbach, Kumar Introduction to Data Mining 4/18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Classification: Definition Given a collection of records (training set ) – Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. – A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it

Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class . earning algorithm Medium100KNo Medium Induction Large 220K Learn Model 75K 10 No Small 90K Training Set Model Tid Attrib1 Attrib2 Attrib3 Class Model 11 No Small Medium 80K Deduction 15 No 67K7 Test Set C Tan, Steinbach, Kumar Introduction to Data Mining 18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Illustrating Classification Task Apply Model Induction Deduction Learn Model Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K ? 12 Yes Medium 80K ? 13 Yes Large 110K ? 14 No Small 95K ? 15 No Large 67K ? 10 Test Set Learning algorithm Training Set

Examples of Classification Task Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil Categorizing news stories as finance, weather, entertainment, sports, etc C Tan, Steinbach, Kumar Introduction to Data Mining 4/18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Examples of Classification Task Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil Categorizing news stories as finance, weather, entertainment, sports, etc

Classification Techniques Decision Tree based Methods Rule-based methods Memory based reasoning Neural Networks Naive Bayes and Bayesian Belief Networks Support Vector Machines C Tan, Steinbach, Kumar Introduction to Data Mining 4/18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Classification Techniques Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines

Example of a decision Tree Splitting Attributes Tid Refund Marital Taxable Status Income Cheat 1 Y Single 125K No 2No Married 100K No Refund Yes 3No Single 70K No 4 Yes Married 120K No NO MarT 5No Divorced 95K Yes Single, DiVorced Married nO Married60K No 7 Y Divorced220K No TaxIng NO 8No Yes 80K No Married 75K No NO YES 10No Single 90K Yes Training Data Model: decision tree C Tan, Steinbach, Kumar Introduction to Data Mining 4/18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Example of a Decision Tree Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 Refund MarSt TaxInc NO YES NO NO Yes No Single, Divorced Married 80K Splitting Attributes Training Data Model: Decision Tree

Another Example of Decision Tree MasT Single Married Divorced d refund marital Taxable Status Income Cheat NO Refund Single 125K No Yes No Married 100K No 3No Single 70K No NO TaxIne Married 120K No 80K 5No Divorced 95K Yes NO YES 6No Married 60K No Divorced 220K No 8No 85K Yes 9No Married 75K No There could be more than one tree that 10No Single 90KYes fits the same datal C Tan, Steinbach, Kumar Introduction to Data Mining 4/18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Another Example of Decision Tree Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 MarSt Refund TaxInc NO YES NO NO Yes No Married Single, Divorced 80K There could be more than one tree that fits the same data!

Decision tree classification task Tree Tid Attrib1 Attrib2 Attrib3 Class Induction algorith Medium Induction Large 220K Learn Model 90K Training set Model Decision Model ree Attrib3 Cla Deduction 95K C Tan, Steinbach, Kumar Introduction to Data Mining 18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Decision Tree Classification Task Apply Model Induction Deduction Learn Model Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K ? 12 Yes Medium 80K ? 13 Yes Large 110K ? 14 No Small 95K ? 15 No Large 67K ? 10 Test Set Tree Induction algorithm Training Set Decision Tree

Apply Model to Test Data Test Data Start from the root of tree Refund marital Taxable Status Income Cheat No Married 80K Refund Yes NO MasT Single, Diorced Married TaxIng NO 80K >80K NO YES C Tan, Steinbach, Kumar Introduction to Data Mining 18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Apply Model to Test Data Refund MarSt TaxInc NO YES NO NO Yes No Single, Divorced Married 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data Start from the root of tree

Apply Model to Test Data Test Data Refund marital Taxable Status Income Cheat No Married 80K Refund Yes NO MasT Single, Diorced Married TaxIng NO 80K >80K NO YES C Tan, Steinbach, Kumar Introduction to Data Mining 18/2004

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Apply Model to Test Data Refund MarSt TaxInc NO YES NO NO Yes No Single, Divorced Married 80K Refund Marital Status Taxable Income Cheat No Married 80K ? 10 Test Data