数据挖掘双语教学课件7class.pptVIP

Data Mining: Concepts and Techniques Data Mining: Concepts and Techniques — Slides for Textbook — — Chapter 7 — ?Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca Chapter 7. Classification and Prediction What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian Classification Classification by backpropagation Classification based on concepts from association rule mining Other Classification Methods Prediction Classification accuracy Summary Classification vs. Prediction Classification: predicts categorical class labels 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 Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction: training set The model is represented as classification rules, decision trees, or mathematical formulae Model usage: for classifying future or unknown objects Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting will occur Classification Process (1): Model Construction Classification Process (2): Use the Model in Prediction Supervised vs. Unsupervised Learning Supervised learning (classification) Supervision: The training data (observations, measuremen