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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 and towards the end from Chapter 5 Introduction.

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Presentation on theme: "Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 and towards the end from Chapter 5 Introduction."— Presentation transcript:

1 Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 and towards the end from Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar Adapted and modified by Srinivasan Parthasarathy 4/11/2007 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1

2 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Classification: Definition l Given a collection of records (training set ) –Each record contains a set of attributes, one of the attributes is the class. l Find a model for class attribute as a function of the values of other attributes. l 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.

3 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Examples of Classification Task l Classifying credit card transactions as legitimate or fraudulent l Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil l Categorizing news stories as finance, weather, entertainment, sports, etc

4 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Classification Techniques l Decision Tree based Methods l Rule-based Methods l Memory based reasoning l Neural Networks l Naïve Bayes and Bayesian Belief Networks l Support Vector Machines

5 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Example of a Decision Tree categorical continuous class Refund MarSt TaxInc YES NO YesNo Married Single, Divorced < 80K> 80K Splitting Attributes Training Data Model: Decision Tree

6 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Decision Tree Classification Task Decision Tree

7 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Decision Tree Induction l Many Algorithms: –Hunt’s Algorithm (one of the earliest) –CART –ID3, C4.5 –SLIQ,SPRINT

8 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ General Structure of Hunt’s Algorithm l Let D t be the set of training records that reach a node t l General Procedure: –If D t contains records that belong the same class y t, then t is a leaf node labeled as y t –If D t is an empty set, then t is a leaf node labeled by the default class, y d –If D t contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset. DtDt ?

9 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Hunt’s Algorithm Don’t Cheat Refund Don’t Cheat Don’t Cheat YesNo Refund Don’t Cheat YesNo Marital Status Don’t Cheat Single, Divorced Married Taxable Income Don’t Cheat < 80K>= 80K Refund Don’t Cheat YesNo Marital Status Don’t Cheat Single, Divorced Married

10 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Tree Induction l Greedy strategy. –Split the records based on an attribute test that optimizes certain criterion. l Issues –Determine how to split the records  How to specify the attribute test condition?  How to determine the best split? –Determine when to stop splitting

11 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ How to Specify Test Condition? l Depends on attribute types –Nominal –Ordinal –Continuous l Depends on number of ways to split –2-way split –Multi-way split

12 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Splitting Based on Nominal Attributes l Multi-way split: Use as many partitions as distinct values. l Binary split: Divides values into two subsets. Need to find optimal partitioning. CarType Family Sports Luxury CarType {Family, Luxury} {Sports} CarType {Sports, Luxury} {Family} OR

13 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Splitting Based on Continuous Attributes

14 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ How to determine the Best Split l Greedy approach: –Nodes with homogeneous class distribution are preferred l Need a measure of node impurity: Non-homogeneous, High degree of impurity Homogeneous, Low degree of impurity

15 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Measures of Node Impurity l Gini Index l Entropy l Misclassification error

16 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Measure of Impurity: GINI l Gini Index for a given node t : (NOTE: p( j | t) is the relative frequency of class j at node t). –Maximum (1 - 1/n c ) when records are equally distributed among all classes, implying least interesting information –Minimum (0.0) when all records belong to one class, implying most interesting information

17 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Splitting Based on GINI l Used in CART, SLIQ, SPRINT. l When a node p is split into k partitions (children), the quality of split is computed as, where,n i = number of records at child i, n = number of records at node p.

18 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Binary Attributes: Computing GINI Index l Splits into two partitions l Effect of Weighing partitions: –Larger and Purer Partitions are sought for. B? YesNo Node N1Node N2 Gini(N1) = 1 – (5/6) 2 – (2/6) 2 = Gini(N2) = 1 – (1/6) 2 – (4/6) 2 = Gini(Children) = 7/12 * /12 * = 0.333

19 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Categorical Attributes: Computing Gini Index l For each distinct value, gather counts for each class in the dataset l Use the count matrix to make decisions Multi-way splitTwo-way split (find best partition of values)

20 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Continuous Attributes: Computing Gini Index l Use Binary Decisions based on one value l Several Choices for the splitting value –Number of possible splitting values = Number of distinct values l Each splitting value has a count matrix associated with it –Class counts in each of the partitions, A < v and A  v l Simple method to choose best v –For each v, scan the database to gather count matrix and compute its Gini index –Computationally Inefficient! Repetition of work.

21 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Continuous Attributes: Computing Gini Index... l For efficient computation: for each attribute, –Sort the attribute on values –Linearly scan these values, each time updating the count matrix and computing gini index –Choose the split position that has the least gini index Split Positions Sorted Values

22 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Alternative Splitting Criteria based on INFO l Entropy at a given node t: (NOTE: p( j | t) is the relative frequency of class j at node t). –Measures homogeneity of a node.  Maximum (log n c ) when records are equally distributed among all classes implying least information  Minimum (0.0) when all records belong to one class, implying most information –Entropy based computations are similar to the GINI index computations

23 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Examples for computing Entropy P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Entropy = – 0 log 0 – 1 log 1 = – 0 – 0 = 0 P(C1) = 1/6 P(C2) = 5/6 Entropy = – (1/6) log 2 (1/6) – (5/6) log 2 (1/6) = 0.65 P(C1) = 2/6 P(C2) = 4/6 Entropy = – (2/6) log 2 (2/6) – (4/6) log 2 (4/6) = 0.92

24 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Splitting Based on INFO... l Information Gain: Parent Node, p is split into k partitions; n i is number of records in partition i –Measures Reduction in Entropy achieved because of the split. Choose the split that achieves most reduction (maximizes GAIN) –Used in ID3 and C4.5 –Disadvantage: Tends to prefer splits that result in large number of partitions, each being small but pure.

25 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Splitting Based on INFO... l Gain Ratio: Parent Node, p is split into k partitions n i is the number of records in partition i –Adjusts Information Gain by the entropy of the partitioning (SplitINFO). Higher entropy partitioning (large number of small partitions) is penalized! –Used in C4.5 –Designed to overcome the disadvantage of Information Gain

26 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Splitting Criteria based on Classification Error l Classification error at a node t : l Measures misclassification error made by a node.  Maximum (1 - 1/n c ) when records are equally distributed among all classes, implying least interesting information  Minimum (0.0) when all records belong to one class, implying most interesting information

27 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Examples for Computing Error P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Error = 1 – max (0, 1) = 1 – 1 = 0 P(C1) = 1/6 P(C2) = 5/6 Error = 1 – max (1/6, 5/6) = 1 – 5/6 = 1/6 P(C1) = 2/6 P(C2) = 4/6 Error = 1 – max (2/6, 4/6) = 1 – 4/6 = 1/3

28 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Comparison among Splitting Criteria For a 2-class problem:

29 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Tree Induction l Greedy strategy. –Split the records based on an attribute test that optimizes certain criterion. l Issues –Determine how to split the records  How to specify the attribute test condition?  How to determine the best split? –Determine when to stop splitting

30 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Stopping Criteria for Tree Induction l Stop expanding a node when all the records belong to the same class l Stop expanding a node when all the records have similar attribute values l Early termination (to be discussed later)

31 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Decision Tree Based Classification l Advantages: –Inexpensive to construct –Extremely fast at classifying unknown records –Easy to interpret for small-sized trees –Accuracy is comparable to other classification techniques for many simple data sets

32 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Example: C4.5 l Simple depth-first construction. l Uses Information Gain l Sorts Continuous Attributes at each node. l Needs entire data to fit in memory. l Unsuitable for Large Datasets. –Needs out-of-core sorting. l You can download the software from:

33 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Practical Issues of Classification l Underfitting and Overfitting l Missing Values l Costs of Classification

34 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Underfitting and Overfitting Overfitting Underfitting: when model is too simple, both training and test errors are large

35 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Overfitting due to Noise Decision boundary is distorted by noise point

36 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Overfitting due to Insufficient Examples Lack of data points in the lower half of the diagram makes it difficult to predict correctly the class labels of that region - Insufficient number of training records in the region causes the decision tree to predict the test examples using other training records that are irrelevant to the classification task

37 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Notes on Overfitting l Overfitting results in decision trees that are more complex than necessary l Training error no longer provides a good estimate of how well the tree will perform on previously unseen records l Need new ways for estimating errors

38 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Estimating Generalization Errors l Re-substitution errors: error on training (  e(t) ) l Generalization errors: error on testing (  e’(t)) l Methods for estimating generalization errors: –Optimistic approach: e’(t) = e(t) –Pessimistic approach:  For each leaf node: e’(t) = (e(t)+0.5)  Total errors: e’(T) = e(T) + N  0.5 (N: number of leaf nodes)  For a tree with 30 leaf nodes and 10 errors on training (out of 1000 instances): Training error = 10/1000 = 1% Generalization error = (  0.5)/1000 = 2.5% –Reduced error pruning (REP):  uses validation data set to estimate generalization error

39 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ How to Address Overfitting l Pre-Pruning (Early Stopping Rule) –Stop the algorithm before it becomes a fully-grown tree –Typical stopping conditions for a node:  Stop if all instances belong to the same class  Stop if all the attribute values are the same –More restrictive conditions:  Stop if number of instances is less than some user-specified threshold  Stop if class distribution of instances are independent of the available features (e.g., using  2 test)  Stop if expanding the current node does not improve impurity measures (e.g., Gini or information gain).

40 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ How to Address Overfitting… l Post-pruning –Grow decision tree to its entirety –Trim the nodes of the decision tree in a bottom-up fashion –If generalization error improves after trimming, replace sub-tree by a leaf node. –Class label of leaf node is determined from majority class of instances in the sub-tree –Can use MDL for post-pruning

41 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Example of Post-Pruning Class = Yes20 Class = No10 Error = 10/30 Training Error (Before splitting) = 10/30 Pessimistic error = ( )/30 = 10.5/30 Training Error (After splitting) = 9/30 Pessimistic error (After splitting) = (9 + 4  0.5)/30 = 11/30 PRUNE! Class = Yes8 Class = No4 Class = Yes3 Class = No4 Class = Yes4 Class = No1 Class = Yes5 Class = No1

42 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Examples of Post-pruning –Optimistic error? –Pessimistic error? –Reduced error pruning? C0: 11 C1: 3 C0: 2 C1: 4 C0: 14 C1: 3 C0: 2 C1: 2 Don’t prune for both cases Don’t prune case 1, prune case 2 Case 1: Case 2: Depends on validation set

43 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Occam’s Razor l Given two models of similar generalization errors, one should prefer the simpler model over the more complex model l For complex models, there is a greater chance that it was fitted accidentally by errors in data l Therefore, one should include model complexity when evaluating a model

44 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Handling Missing Attribute Values l Missing values affect decision tree construction in three different ways: –Affects how impurity measures are computed –Affects how to distribute instance with missing value to child nodes –Affects how a test instance with missing value is classified l While the book describes a few ways it can be handled as part of the process – it is often best to handle this using standard statistical methods –EM-based estimation

45 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Other Issues l Data Fragmentation l Search Strategy l Expressiveness

46 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Data Fragmentation l Number of instances gets smaller as you traverse down the tree l Number of instances at the leaf nodes could be too small to make any statistically significant decision

47 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Search Strategy l Finding an optimal decision tree is NP-hard l The algorithm presented so far uses a greedy, top-down, recursive partitioning strategy to induce a reasonable solution l Other strategies? –Bottom-up –Bi-directional

48 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Expressiveness l Decision tree provides expressive representation for learning discrete-valued function –But they do not generalize well to certain types of Boolean functions  Example: XOR or Parity functions (example in book) l Not expressive enough for modeling continuous variables –Particularly when test condition involves only a single attribute at-a-time

49 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Expressiveness: Oblique Decision Trees x + y < 1 Class = + Class = Test condition may involve multiple attributes More expressive representation Finding optimal test condition is computationally expensive Needs multi-dimensional discretization

50 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Model Evaluation l Metrics for Performance Evaluation –How to evaluate the performance of a model? l Methods for Performance Evaluation –How to obtain reliable estimates? l Methods for Model Comparison –How to compare the relative performance among competing models?

51 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Metrics for Performance Evaluation l Focus on the predictive capability of a model –Rather than how fast it takes to classify or build models, scalability, etc. l Confusion Matrix: PREDICTED CLASS ACTUAL CLASS Class=YesClass=No Class=Yesab Class=Nocd a: TP (true positive) b: FN (false negative) c: FP (false positive) d: TN (true negative)

52 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Metrics for Performance Evaluation… l Most widely-used metric: PREDICTED CLASS ACTUAL CLASS Class=YesClass=No Class=Yesa (TP) b (FN) Class=Noc (FP) d (TN)

53 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Limitation of Accuracy l Consider a 2-class problem –Number of Class 0 examples = 9990 –Number of Class 1 examples = 10 l If model predicts everything to be class 0, accuracy is 9990/10000 = 99.9 % –Accuracy is misleading because model does not detect any class 1 example

54 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Cost Matrix PREDICTED CLASS ACTUAL CLASS C(i|j) Class=YesClass=No Class=YesC(Yes|Yes)C(No|Yes) Class=NoC(Yes|No)C(No|No) C(i|j): Cost of misclassifying class j example as class i

55 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Computing Cost of Classification Cost Matrix PREDICTED CLASS ACTUAL CLASS C(i|j) Model M 1 PREDICTED CLASS ACTUAL CLASS Model M 2 PREDICTED CLASS ACTUAL CLASS Accuracy = 80% Cost = 3910 Accuracy = 90% Cost = 4255

56 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Cost-Sensitive Measures l Precision is biased towards C(Yes|Yes) & C(Yes|No) l Recall is biased towards C(Yes|Yes) & C(No|Yes) l F-measure is biased towards all except C(No|No)

57 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Methods for Performance Evaluation l How to obtain a reliable estimate of performance? l Performance of a model may depend on other factors besides the learning algorithm: –Class distribution –Cost of misclassification –Size of training and test sets

58 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Learning Curve l Learning curve shows how accuracy changes with varying sample size l Requires a sampling schedule for creating learning curve: l Arithmetic sampling (Langley, et al) l Geometric sampling (Provost et al) Effect of small sample size: - Bias in the estimate - Variance of estimate

59 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Methods of Estimation l Holdout –Reserve 2/3 for training and 1/3 for testing l Random subsampling –Repeated holdout l Cross validation –Partition data into k disjoint subsets –k-fold: train on k-1 partitions, test on the remaining one –Leave-one-out: k=n l Stratified sampling –oversampling vs undersampling l Bootstrap –Sampling with replacement

60 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Model Evaluation l Metrics for Performance Evaluation –How to evaluate the performance of a model? l Methods for Performance Evaluation –How to obtain reliable estimates? l Methods for Model Comparison –How to compare the relative performance among competing models?

61 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ ROC Curve (TP,FP): l (0,0): declare everything to be negative class l (1,1): declare everything to be positive class l (1,0): ideal l Diagonal line: –Random guessing –Below diagonal line:  prediction is opposite of the true class

62 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Using ROC for Model Comparison l No model consistently outperform the other l M 1 is better for small FPR l M 2 is better for large FPR l Area Under the ROC curve l Ideal:  Area = 1 l Random guess:  Area = 0.5

63 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Other Classifiers (Chapter 5) Bayesian Classification l Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical approaches to certain types of learning problems l Incremental: Each training example can incrementally increase/decrease the probability that a hypothesis is correct. Prior knowledge can be combined with observed data. l Probabilistic prediction: Predict multiple hypotheses, weighted by their probabilities l Standard: Even when Bayesian methods are computationally intractable, they can provide a standard of optimal decision making against which other methods can be measured

64 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Bayesian Theorem: Basics l Let X be a data sample whose class label is unknown l Let H be a hypothesis that X belongs to class C l For classification problems, determine P(H/X): the probability that the hypothesis holds given the observed data sample X l P(H): prior probability of hypothesis H (i.e. the initial probability before we observe any data, reflects the background knowledge) l P(X): probability that sample data is observed l P(X|H) : probability of observing the sample X, given that the hypothesis holds

65 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Bayes Theorem (Recap) l Given training data X, posteriori probability of a hypothesis H, P(H|X) follows the Bayes theorem l MAP (maximum posteriori) hypothesis l Practical difficulty: require initial knowledge of many probabilities, significant computational cost; insufficient data

66 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Naïve Bayes Classifier l A simplified assumption: attributes are conditionally independent: l 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) l No dependence relation between attributes l Greatly reduces the computation cost, only count the class distribution. l Once the probability P(X|C i ) is known, assign X to the class with maximum P(X|C i )*P(C i )

67 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Training dataset Class: C1:buys_computer= ‘yes’ C2:buys_computer= ‘no’ Data sample X =(age<=30, Income=medium, Student=yes Credit_rating= Fair)

68 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Naïve 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<=30,income =medium, student=yes,credit_rating=fair) P(X|Ci) : P(X|buys_computer=“yes”)= x x x =0.044 P(X|buys_computer=“no”)= 0.6 x 0.4 x 0.2 x 0.4 =0.019 Multiply by P(Ci)s and we can conclude that X belongs to class “buys_computer=yes”

69 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Naïve Bayesian Classifier: Comments l Advantages : –Easy to implement –Good results obtained in most of the cases l Disadvantages –Assumption: class conditional independence, therefore loss of accuracy –Practically, dependencies exist among variables –E.g., hospitals: patients: Profile: age, family history etc Symptoms: fever, cough etc., Disease: lung cancer, diabetes etc –Dependencies among these cannot be modeled by Naïve Bayesian Classifier l How to deal with these dependencies? –Bayesian Belief Networks

70 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Classification Using Distance l Place items in class to which they are “closest”. l Must determine distance between an item and a class. l Classes represented by –Centroid: Central value. –Medoid: Representative point. –Individual points l Algorithm: KNN

71 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ K Nearest Neighbor (KNN): l Training set includes classes. l Examine K items near item to be classified. l New item placed in class with the most number of close items. l O(q) for each tuple to be classified. (Here q is the size of the training set.)

72 © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ KNN


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