数据挖掘课件数据挖掘06.pdf

Data Mining:
Concepts and Techniques
— Chapter 6 —
Jiawei Han
Department puter Science
University of Illinois at Urbana-Champaign
/~hanj
©2006 Jiawei Han and Micheline Kamber, All rights reserved
2011-1-19 School of Management, HUST 1
2011-1-19 School of Management, HUST 2
Chapter 6. Classification and Prediction
 What is classification? What is  Support Vector Machines (SVM)
prediction?  Associative classification
 Issues regarding classification  Lazy learners (or learning from
and prediction your neighbors)
 Classification by decision tree  Other classification methods
induction
 Prediction
 Bayesian classification
 Accuracy and error measures
 Rule-based classification
 Ensemble methods
 Classification by back
 Model selection
propagation
 Summary
2011-1-19 School of Management, HUST 3
Classification vs. Prediction
 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, ., predicts
unknown or missing values
 Typical applications
 Credit approval
 Target marketing
 Medical diagnosis
 Fraud detection
2011-1-19 School of Management, HUST 4
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 is 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 pared with the
classified result from the model
 Accuracy rate is the percentage of test set sa