Data Mining Classification: Basic Concepts and Techniques Lecture - PDF document

Data Mining Classification: Basic Concepts and Techniques Lecture Notes for Chapter 3 Introduction to Data Mining, 2 nd Edition by Tan, Steinbach, Karpatne, Kumar Introduction to Data Mining, 2 nd Edition 09/21/2020 1 1 Classification: Definition  Given a collection of records (training set ) – Each record is by characterized by a tuple ( x , y ), where x is the attribute set and y is the class label  x : attribute, predictor, independent variable, input  y : class, response, dependent variable, output  Task: – Learn a model that maps each attribute set x into one of the predefined class labels y Introduction to Data Mining, 2 nd Edition 09/21/2020 2 2

Examples of Classification Task Task Attribute set, x Class label, y Categorizing Features extracted from spam or non-spam email email message header messages and content Identifying Features extracted from malignant or benign tumor cells x-rays or MRI scans cells Cataloging Features extracted from Elliptical, spiral, or galaxies telescope images irregular-shaped galaxies Introduction to Data Mining, 2 nd Edition 09/21/2020 3 3 General Approach for Building Classification Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No No 3 No Small 70K 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No Learn 7 Yes Large 220K No Model Yes 8 No Small 85K 9 No Medium 75K No 10 No Small 90K Yes 10 Apply Model 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 Introduction to Data Mining, 2 nd Edition 09/21/2020 4 4

Classification Techniques  Base Classifiers – Decision Tree based Methods – Rule-based Methods – Nearest-neighbor – Neural Networks, Deep Neural Nets – Naïve Bayes and Bayesian Belief Networks – Support Vector Machines  Ensemble Classifiers – Boosting, Bagging, Random Forests Introduction to Data Mining, 2 nd Edition 09/21/2020 5 5 Example of a Decision Tree Splitting Attributes Home Marital Annual Defaulted ID Owner Status Income Borrower 1 Yes Single 125K No Home Owner 2 No Married 100K No Yes No 3 No Single 70K No 4 Yes Married 120K No NO MarSt Married 5 No Divorced 95K Yes Single, Divorced 6 No Married 60K No Income NO 7 Yes Divorced 220K No < 80K > 80K 8 No Single 85K Yes 9 No Married 75K No NO YES 10 No Single 90K Yes 10 Model: Decision Tree Training Data Introduction to Data Mining, 2 nd Edition 09/21/2020 6 6

Another Example of Decision Tree Single, MarSt Married Divorced Home Marital Annual Defaulted ID Owner Status Income Borrower NO Home 1 Yes Single 125K No No Owner Yes 2 No Married 100K No NO Income 3 No Single 70K No 4 Yes Married 120K No < 80K > 80K 5 No Divorced 95K Yes NO YES 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes There could be more than one tree that 9 No Married 75K No fits the same data! 10 No Single 90K Yes 1 0 Introduction to Data Mining, 2 nd Edition 09/21/2020 7 7 Apply Model to Test Data Test Data Start from the root of tree. Home Marital Annual Defaulted Owner Status Income Borrower No Married 80K ? Home 10 Owner Yes No NO MarSt Married Single, Divorced Income NO < 80K > 80K NO YES Introduction to Data Mining, 2 nd Edition 09/21/2020 8 8

Apply Model to Test Data Test Data Home Marital Annual Defaulted Owner Status Income Borrower No Married 80K ? Home 10 Owner Yes No NO MarSt Married Single, Divorced Income NO < 80K > 80K NO YES Introduction to Data Mining, 2 nd Edition 09/21/2020 9 9 Apply Model to Test Data Test Data Home Marital Annual Defaulted Owner Status Income Borrower No Married 80K ? Home 10 Owner Yes No NO MarSt Married Single, Divorced Income NO < 80K > 80K NO YES Introduction to Data Mining, 2 nd Edition 09/21/2020 10 10

Apply Model to Test Data Test Data Home Marital Annual Defaulted Owner Status Income Borrower No Married 80K ? Home 10 Owner Yes No NO MarSt Married Single, Divorced Income NO < 80K > 80K NO YES Introduction to Data Mining, 2 nd Edition 09/21/2020 11 11 Apply Model to Test Data Test Data Home Marital Annual Defaulted Owner Status Income Borrower No Married 80K ? Home 10 Owner Yes No NO MarSt Married Single, Divorced Income NO < 80K > 80K NO YES Introduction to Data Mining, 2 nd Edition 09/21/2020 12 12

Apply Model to Test Data Test Data Home Marital Annual Defaulted Owner Status Income Borrower No Married 80K ? Home 10 Owner Yes No NO MarSt Assign Defaulted to Married Single, Divorced “No” Income NO < 80K > 80K NO YES Introduction to Data Mining, 2 nd Edition 09/21/2020 13 13 Decision Tree Classification Task 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 Yes 5 No Large 95K 6 No Medium 60K No Learn 7 Yes Large 220K No Model 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Apply Decision Model Tree 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 Introduction to Data Mining, 2 nd Edition 09/21/2020 14 14

Decision Tree Induction  Many Algorithms: – Hunt’s Algorithm (one of the earliest) – CART – ID3, C4.5 – SLIQ,SPRINT Introduction to Data Mining, 2 nd Edition 09/21/2020 15 15 General Structure of Hunt’s Algorithm  Let D t be the set of training Home Marital Annual Defaulted ID Owner Status Income Borrower records that reach a node t 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No  General Procedure: 4 Yes Married 120K No – If D t contains records that 5 No Divorced 95K Yes belong the same class y t , 6 No Married 60K No then t is a leaf node 7 Yes Divorced 220K No 8 No Single 85K Yes labeled as y t 9 No Married 75K No – If D t contains records that 10 No Single 90K Yes belong to more than one 10 D t class, use an attribute test to split the data into smaller ? subsets. Recursively apply the procedure to each subset. Introduction to Data Mining, 2 nd Edition 09/21/2020 16 16

Hunt’s Algorithm Home Marital Annual Defaulted ID Owner Status Income Borrower 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No (7,3) (3,0) (4,3) 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 (3,0) (3,0) (3,0) (1,3) (3,0) (1,0) (0,3) Introduction to Data Mining, 2 nd Edition 09/21/2020 17 17 Hunt’s Algorithm Home Marital Annual Defaulted ID Owner Status Income Borrower 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No (7,3) (3,0) (4,3) 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 (3,0) (3,0) (3,0) (1,3) (3,0) (1,0) (0,3) Introduction to Data Mining, 2 nd Edition 09/21/2020 18 18

Hunt’s Algorithm Home Marital Annual Defaulted ID Owner Status Income Borrower 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No (7,3) (3,0) (4,3) 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 (3,0) (3,0) (3,0) (1,3) (3,0) (1,0) (0,3) Introduction to Data Mining, 2 nd Edition 09/21/2020 19 19 Hunt’s Algorithm Home Marital Annual Defaulted ID Owner Status Income Borrower 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No (7,3) (3,0) (4,3) 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 (3,0) (3,0) (3,0) (1,3) (3,0) (1,0) (0,3) Introduction to Data Mining, 2 nd Edition 09/21/2020 20 20

Design Issues of Decision Tree Induction  How should training records be split? – Method for specifying test condition  depending on attribute types – Measure for evaluating the goodness of a test condition  How should the splitting procedure stop? – Stop splitting if all the records belong to the same class or have identical attribute values – Early termination Introduction to Data Mining, 2 nd Edition 09/21/2020 21 21 Methods for Expressing Test Conditions  Depends on attribute types – Binary – Nominal – Ordinal – Continuous  Depends on number of ways to split – 2-way split – Multi-way split Introduction to Data Mining, 2 nd Edition 09/21/2020 22 22