《数据仓库与数据挖掘》第9章.pptx

第7章: 分类和预测;Classification: predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data Prediction: models continuous-valued functions, i.e., predicts unknown or missing values Typical Applications credit approval target marketing medical diagnosis treatment effectiveness analysis;Classification—A Two-Step Process ;Classification Process (1): Model Construction;Classification Process (2): Use the Model in Prediction;Supervised vs. Unsupervised Learning;第7章: 分类和预测;Issues Regarding Classification and Prediction (1): Data Preparation;Issues regarding classification and prediction (2): Evaluating Classification Methods;第7章: 分类和预测;Training Dataset;Output: A Decision Tree for “buys_computer”;Algorithm for Decision Tree Induction;Attribute Selection Measure: Information Gain (ID3/C4.5);Attribute Selection by Information Gain Computation;Other Attribute Selection Measures;Gini Index (IBM IntelligentMiner);Extracting Classification Rules from Trees;Avoid Overfitting in Classification;Approaches to Determine the Final Tree Size;Enhancements to basic decision tree induction;Classification in Large Databases;Scalable Decision Tree Induction Methods in Data Mining Studies;Data Cube-Based Decision-Tree Induction;Presentation of Classification Results;Visualization of a Decision Tree in SGI/MineSet 3.0;Interactive Visual Mining by Perception-Based Classification (PBC);第7章: 分类和预测;Bayesian Classification: Why?;Bayesian Theorem: Basics;Bayesian Theorem;Na?ve Bayes Classifier ;Training dataset;Na?ve Bayesian Classifier: Example;Na?ve Bayesian Classifier: Comments;Bayesian Networks;Bayesian Belief Network: An Example;Learning Bayesian Networks;第7章: 分类和预测;Classification: predicts categorical class labels Typical Applications {credit history, salary}-> credit approval ( Yes/No) {Temp, Humidity} --> Rain (Yes/No);Linear Classificati