数据挖掘导论英文chap4_basic_classification.ppt

(C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation 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 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 Na?ve Bayes and Bayesian Belief Networks Support Vector Machines Example of a Decision Tree Another Example of Decision Tree Decision Tree Classification Task Apply Model to Test Data Apply Model to Test Data Apply Model to Test Data Apply Model to Test Data Apply Model to Test Data Apply Model to Test Data Decision Tree Classification Task Decision Tree Induction Many Algorithms: Hunt’s Algorithm (one of the earliest) CART ID3, C4.5 SLIQ,SPRINT General Structure of Hunt’s Algorithm Let Dt be the set of training records that reach a node t General Procedure: If Dt contains records that belong the same class yt, then t is a leaf node labeled as yt If Dt is an empty set, then t is a leaf node labeled by the default class, yd If Dt contains records that belong to more than one class, use an attribute test to