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