《数据仓库与数据挖掘》第9章.pptx

,Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,Data Mining:Concepts and Techniques,*,第7章:分类和预测,What is classification?What is prediction?,Issues regarding classification and prediction,Classification by decision tree induction,Bayesian Classification,Classification by Neural Networks,Classification by Support Vector Machines(SVM),Classification based on concepts from association rule mining,Other Classification Methods,Prediction,Classification accuracy,Summary,2025/5/17 周六,1,Data Mining:Concepts and Techniques,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 vs.Prediction,2025/5/17 周六,2,Data Mining:Concepts and Techniques,ClassificationA 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 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,If the accuracy is acceptable,use the model to classify data tuples whose class labels are not known,2025/5/17 周六,3,Data Mining:Concepts and Techniques,Classification Process(1):Model Construction,Training,Data,Classification,Algorithms,IF rank=professor,OR years 6,THEN tenured=yes,Classifier,(Model),2025/5/17 周六,4,Data Mining:Concepts and Techniques,Classification Process(2):Use the Model in Prediction,Classifier,Testing,Data,Unseen Data,(,Jeff,Professor,4),Tenured?,2025/5/17 周六,5,Data Mining:Concepts and Techniques,Supervised vs.Unsupervised Learning,Supervised learning(classification),Supervision:The training data(observations,measurements,etc.)are accompanied by labels indicating the class of the observations,New data is classified based on the training set,Unsupervised learning,(clustering),The class labels of training data is unknown,Given a set of measurements,observations,etc.with the aim of establishing the existence of classes or clusters in the data,2025/5/17 周六,6,Data Mining:Concepts and Techniques,第7章:分类和预测,What is classification?What is prediction?,Issues regarding classification and prediction,Classification by decision tree induction,Bayesian Classification,Classification by Neural Networks,Classification by Support Vector Machines(SVM),Classification based on concepts from association rule mining,Other Classification Methods,Prediction,Classification accuracy,Summary,2025/5/17 周六,7,Data Mining:Concepts and Techniques,Issues Regarding Classification and Prediction(1):Data Preparation,Data cleaning,Preprocess data in order to reduce noise and handle missing values,Relevance analysis(feature selection),Remove the irrelevant or redundant attributes,Data transformation,Generalize and/or normalize data,2025/5/17 周六,8,Data Mining:Concepts and Techniques,Issues regarding classification and prediction(2):Evaluating Classification Methods,Predictive accuracy,Speed and scalability,time to construct the model,time to use the model,Robustness,handling noise and missing values,Scalability,efficiency in disk-resident databases,Interpretability:,understanding and insight provided by the model,Goodness of rules,decision tree size,compactness of classification rules,2025/5/17 周六,9,Data Mining:Concepts and Techniques,第7章:分类和预测,What is classification?What is prediction?,Issues regarding classification and prediction,Classification by decision tree induction,Bayesian Classification,Classification by Neural Networks,Classification by Support Vector Machines(SVM),Classification based on concepts from association rule mining,Other Classification Methods,Prediction,Classification accuracy,Summary,2025/5/17 周六,10,Data Mining:Concepts and Techniques,Training Dataset,This follows an example from Quinlans ID3,2025/5/17 周六,11,Data Mining:Concepts and Techniques,Output:A Decision Tree for“,buys_computer”,age?,overcast,student?,credit rating?,no,yes,fair,excellent,40,no,no,yes,yes,yes,30.40,2025/5/17 周六,12,Data Mining:Concepts and Techniques,Algorithm for Decision Tree Induction,Basic algorithm(a greedy algorithm),Tree is constructed in a,top-down recursive divide-and-conquer manner,At start,all the training examples are at the root,Attributes are categorical(if continuous-valued,they are discretized in advance),Examples are partitioned recursively based on selected attributes,Test attributes are selected on the basis of a heuristic or statistical measure(e.g.,information gain,),Conditions for stopping partitioning,All samples for a given node belong to the same class,There are no remaining attributes for further partitioning,majority voting,is employed for classifying the leaf,There are no samples left,2025/5/17 周六,13,Data Mining:Concepts and Techniques,Attribute Selection Measure:Information Gain(ID3/C4.5),Select the attribute with the highest information gain,S contains s,i,tuples of class C,i,for i=1,m,information,measures info required to classify any arbitrary tuple,entropy,of attribute A with values a,1,a,2,a,v,information gained,by branching on attribute A,2025/5/17 周六,14,Data Mining:Concepts and Techniques,Attribute Selection by Information Gain Computation,Class P:buys_computer=“yes”,Class N:buys_computer=“no”,I(p,n)=I(9,5)=0.940,Compute the entropy for,age,:,means“age=30”has 5 out of 14 samples,with 2 yeses and 3 nos.Hence,Similarly,2025/5/17 周六,15,Data Mining:Concepts and Techniques,Other Attribute Selection Measures,Gini index,(CART,IBM IntelligentMiner),All attributes are assumed continuous-valued,Assume there exist several possible split values for each attribute,May need other tools,such as clustering,to get the possible split values,Can be modified for categorical attributes,2025/5/17 周六,16,Data Mining:Concepts and Techniques,Gini,Index(IBM IntelligentMiner),If a data set,T,contains examples from,n,classes,gini index,gini,(,T,)is defined as,where,p,j,is the relative frequency of class,j,in,T.,If a data set,T,is split into two subsets,T,1,and,T,2,with sizes,N,1,and,N,2,respectively,the,gini,index of the split data contains examples from,n,classes,the,gini,index,gini,(,T,)is defined as,The attribute provides the smallest,gini,split,(,T,)is chosen to split the node(,need to enumerate all possible splitting points for each attribute,).,2025/5/17 周六,17,Data Mining:Concepts and Techniques,Extracting Classification Rules from Trees,Represent the knowledge in the form of,IF-THEN,rules,One rule is created for each path from the root to a leaf,Each attribute-value pair along a path forms a conjunction,The leaf node holds the class prediction,Rules are easier for humans to understand,Example,IF,age,=“=30”AND,student,=“,no,”THEN,buys_computer,=“,no,”,IF,age,=“40”AND,credit_rating,=“,excellent,”THEN,buys_computer,=“,yes,”,IF,age,=“=30”AND,credit_rating,=“,fair,”THEN,buys_computer,=“,no,”,2025/5/17 周六,18,Data Mining:Concepts and Techniques,Avoid Overfitting in Classification,Overfitting:An induced tree may overfit the training data,Too many branches,some may reflect anomalies due to noise or outliers,Poor accuracy for unseen samples,Two approaches to avoid overfitting,Prepruning:Halt tree construction earlydo not split a node if this would result in the goodness measure falling below a threshold,Difficult to choose an appropriate threshold,Postpruning:Remove branches from a“fully grown”treeget a sequence of progressively pruned trees,Use a set of data different from the training data to decide which is the“best pruned tree”,2025/5/17 周六,19,Data Mining:Concepts and Techniques,Approaches to Determine the Final Tree Size,Separate training(2/3)and testing(1/3)sets,Use cross validation,e.g.,10-fold cross validation,Use all the data for training,but apply a,statistical test,(e.g.,chi-square)to estimate whether expanding or pruning a node may improve the entire distribution,Use minimum description length(MDL)principle,halting growth of the tree when the encoding is minimized,2025/5/17 周六,20,Data Mining:Concepts and Techniques,Enhancements to basic decision tree induction,Allow for continuous-valued attributes,Dynamically define new discrete-valued attributes that partition the continuous attribute value into a discrete set of intervals,Handle missing attribute values,Assign the most common value of the attribute,Assign probability to each of the possible values,Attribute construction,Create new attributes based on existing ones that are sparsely represented,This reduces fragmentation,repetition,and replication,2025/5/17 周六,21,Data Mining:Concepts and Techniques,Classification in Large Databases,Classificationa classical problem extensively studied by statisticians and machine learning researchers,Scalability:Classifying data sets with millions of examples and hundreds of attributes with reasonable speed,Why decision tree induction in data mining?,relatively faster learning speed(than other classification methods),convertible to simple and easy to understand classification rules,can use SQL queries for accessing databases,comparable classification accuracy with other methods,2025/5/17 周六,22,Data Mining:Concepts and Techniques,Scalable Decision Tree Induction Methods in Data Mining Studies,SLIQ,(EDBT96 Mehta et al.),builds an index for each attribute and only class list and the current attribute list reside in memory,SPRINT,(VLDB96 J.Shafer et al.),constructs an attribute list data structure,PUBLIC,(VLDB98 Rastogi&Shim),integrates tree splitting and tree pruning:stop growing the tree earlier,RainForest,(VLDB98 Gehrke,Ramakrishnan&Ganti),separates the scalability aspects from the criteria that determine the quality of the tree,builds an AVC-list(attribute,value,class label),2025/5/17 周六,23,Data Mining:Concepts and Techniques,Data Cube-Based Decision-Tree Induction,Integration of generalization with decision-tree induction(Kamber et al97).,Classification at primitive concept levels,E.g.,precise temperature,humidity,outlook,etc.,Low-level concepts,scattered classes,bushy classification-trees,Semantic interpretation problems.,Cube-based multi-level classification,Relevance analysis at multi-levels.,Information-gain analysis with dimension+level.,2025/5/17 周六,24,Data Mining:Concepts and Techniques,Presentation of Classification Results,2025/5/17 周六,25,Data Mining:Concepts and Techniques,Visualization of a,Decision Tree,in SGI/MineSet 3.0,2025/5/17 周六,26,Data Mining:Concepts and Techniques,Interactive Visual Mining,by Perception-Based Classification(PBC),2025/5/17 周六,27,Data Mining:Concepts and Techniques,第7章:分类和预测,What is classification?What is prediction?,Issues regarding classification and prediction,Classification by decision tree induction,Bayesian Classification,Classification by Neural Networks,Classification by Support Vector Machines(SVM),Classification based on concepts from association rule mining,Other Classification Methods,Prediction,Classification accuracy,Summary,2025/5/17 周六,28,Data Mining:Concepts and Techniques,Bayesian Classification:Why?,Probabilistic learning,:Calculate explicit probabilities for hypothesis,among the most practical approaches to certain types of learning problems,Incremental,:Each training example can incrementally increase/decrease the probability that a hypothesis is correct.Prior knowledge can be combined with observed data.,Probabilistic prediction,:Predict multiple hypotheses,weighted by their probabilities,Standard,:Even when Bayesian methods are computationally intractable,they can provide a standard of optimal decision making against which other methods can be measured,2025/5/17 周六,29,Data Mining:Concepts and Techniques,Bayesian Theorem:Basics,Let X be a data sample whose class label is unknown,Let H be a hypothesis that X belongs to class C,For classification problems,determine P(H/X):the probability that the hypothesis holds given the observed data sample X,P(H):prior probability of hypothesis H(i.e.the initial probability before we observe any data,reflects the background knowledge),P(X):probability that sample data is observed,P(X|H):probability of observing the sample X,given that the hypothesis holds,2025/5/17 周六,30,Data Mining:Concepts and Techniques,Bayesian Theorem,Given training data,X,posteriori probability of a hypothesis H,P(H|X),follows the Bayes theorem,Informally,this can be written as,posterior=likelihood x prior/evidence,MAP(maximum posteriori)hypothesis,Practical difficulty:require initial knowledge of many probabilities,significant computational cost,2025/5/17 周六,31,Data Mining:Concepts and Techniques,Nave Bayes Classifier,A simplified assumption:attributes are conditionally independent:,The product of occurrence of say 2 elements x,1,and x,2,given the current class is C,is the product of the probabilities of each element taken separately,given the same class P(y,1,y,2,C)=P(y,1,C)*P(y,2,C),No dependence relation between attributes,Greatly reduces the computation cost,only count the class distribution.,Once the probability P(X|C,i,)is known,assign X to the class with maximum P(X|C,i,)*P(C,i,),2025/5/17 周六,32,Data Mining:Concepts and Techniques,Training dataset,Class:,C1:buys_computer=,yes,C2:buys_computer=,no,Data sample,X=(age=30,Income=medium,Student=yes,Credit_rating=,Fair),2025/5/17 周六,33,Data Mining:Concepts and Techniques,Nave Bayesian Classifier:Example,Compute P(X/Ci)for each class,P(age=“30”|buys_computer=“yes”)=2/9=0.222,P(age=“30”|buys_computer=“no”)=3/5=0.6,P(income=“medium”|buys_computer=“yes”)=4/9=0.444,P(income=“medium”|buys_computer=“no”)=2/5=0.4,P(student=“yes”|buys_computer=“yes)=6/9=0.667,P(student=“yes”|buys_computer=“no”)=1/5=0.2,P(credit_rating=“fair”|buys_computer=“yes”)=6/9=0.667,P(credit_rating=“fair”|buys_computer=“no”)=2/5=0.4,X=(age credit approval(Yes/No),Temp,Humidity-Rain(Yes/No),Classification,Mathematically,2025/5/17 周六,40,Data Mining:Concepts and Techniques,Linear Classification,Binary Classification problem,The data above the red line belongs to class x,The data below red line belongs to class o,Examples SVM,Perceptron,Probabilistic Classifiers,x,x,x,x,x,x,x,x,x,x,o,o,o,o,o,o,o,o,o,o,o,o,o,2025/5/17 周六,41,Data Mining:Concepts and Techniques,Discriminative Classifiers,Advantages,prediction accuracy is generally high,(as compared to Bayesian methods in general),robust,works when training examples co