1. CSE 8392 Spring 199951 CSE 8392 SPRING 1999 DATA MINING: CORE TOPICS Classification Professor Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Dallas, Texas 75275 (214) 768-3087 fax: (214) 768-3085 email: mhd@seas.smu.edu www: http://www.seas.smu.edu/~mhd January 1999

2. CSE 8392 Spring 199952 “Classify a set of data based on their values in certain attributes” (R[2],p868) Each grouping is a class Equivalence Class Given a set K = {k 1, k 2, k 3 …k y } k x -> (A, B, C, D, E…) where this mapping partitions K Similar to estimation Classification

3. CSE 8392 Spring 199953 Classification Examples Financial market trends (bull, bear) Images (raster, vector) Loan approval (yes, no) Medical diagnosis Detecting faults in industry applications

4. CSE 8392 Spring 199954 Basic Classification Techniques (Kennedy,Ch3) Boundaries for decision regions –Ex: Loan threshold Probability Techniques –p(x|C) –Figure 3-4, p 3-5, Kennedy –May also use prior knowledge of class membership probabilities P(C) –Select class that maximizes P(C)p(x|C) –Probability x is in class C is proportional to the probability that any input is in C and class C contains x

5. CSE 8392 Spring 199955 Supervised Induction Techniques Output the probability of class membership based on input values using some estimation technique (Decision Trees, Neural Nets) –Use a sample database as a training set –Analyze training data –Develop model using attributes of data –Use these class descriptions on rest of data Note: May be many different class descriptions

6. CSE 8392 Spring 199956 Posterior Probabilities (Kennedy) P(C|x) - Probability input x belongs to class C Suppose m classes, look at: P(C 1 |x), …, P(C m |x) Classification by assigning x to the class with the highest posterior probability Look at training data and assign posterior probabilities to example patterns May not work well with complex tuples in large databases Fig 3-7, p3-9

7. CSE 8392 Spring 199957 Linear Regression Linear mapping of input attributes to desired output Error may exist: Here x i are input attributes Least-Squares Minimization: Sum of the squared error terms is minimized over database (training set) Find weights: EQ3,EQ4 Kennedy, p 10-14 May be used as baseline comparison approach May not work well with complex databases : Not all data values known & May not be numeric values

8. CSE 8392 Spring 199958 Similarity Measures Describe each input tuple as vector D 1 = Define Sim(D 1,D 2 ) where: –Normalize (0-no similarity, 1- identical) –Usually assumes all values are numeric only Represent each class with vector C i –May be determined as centroid of vectors from training set Assign each tuple D j to class i where Sim(D j,C i ) is minimized

9. CSE 8392 Spring 199959 K Nearest Neighbors (Kennedy) Store all input-output pairs in training set Define distance function When a tuple needs to be classified, determine distance between it and all items in the training set Fig 10-12, p10-38 Base answer on K nearest items in training set. Algorithm p 10-39 Memory intensive & Slow

10. CSE 8392 Spring 199960 Decision Trees (Kennedy, Section 10.4.10) Similar to Twenty Questions: Fig 8-5, p144, Barquin Internal Nodes: Decision Points based on one attribute Leaves: Identify classes Classification Process: Input tuple and move through tree based on attribute values Difficult Part - Constructing tree (so that it is efficient) (Try playing twenty questions with a young child!) Training Set used to build tree

11. CSE 8392 Spring 199961 Decision Tree Issues Attributes Splits (Categorical, Discrete, Continuous) Ordering of attributes in tree Determining when to stop Must perform perfectly on training set Pruning of tree (remove branches)

12. CSE 8392 Spring 199962 Decision Tree Advantages/Disadvantages Advantages –Easy to understand –Efficient (time) Disadvantages –May be difficult to use with continuous data –Limited to problems solved by dividing into subrectangles –Not flexible (no automatic revisions if incorrect) –No way to handle missing data –May have overfitting –Pruning combats overfitting (but it may induce other errors)

13. CSE 8392 Spring 199963 ID-3 (R[2]) Decision Tree learning system based on information theory Attempts to minimize expected number of tests on a tuple Formalizes the approach adults have to twenty questions! Picks attributes with highest information gain first Entropy

14. CSE 8392 Spring 199964 CART (Kennedy,p10-56) Builds binary decision tree Exhaustive search to determine best tree where best defined by goodness of split : Optimal splitting:

15. CSE 8392 Spring 199965 Neural Networks Determine “predictions using ‘neurons’ (computations) and their interconnections (weighted inputs).” (p142,Barquin) Example: Fig 8-4, p143, Barquin Input values of attributes at left Weight associated with links between nodes Classification produced at output on right Neural Net and Decision Tree Peter Cabena, Pablo Hadjinian, Rolf Stadler, Jaap Verhees, and Alessandro Zanasi, Discovering Data Mining From Concept to Implementation, Prentice-Hall, 1998, p71,p74.

16. CSE 8392 Spring 199966 Neural Nets Number of processing layers between input and output Each processing unit (node) connected to all in the next layer Construct neural net: Determine network structure (modeler) Weights “learned” by applying tree to training set Backpropagation used to adjust weights Desired output provided with training data Actual network output subtracted from desired output and error produced Connection weights changed based on a minimization method called gradient descent

17. CSE 8392 Spring 199967 Historical Note: Neural network weights are adjusted based on whether or not the prediction is good. Earlier IR systems uses “feedback” to adjust weights in document vectors based on precision/recall values

18. CSE 8392 Spring 199968 Advantages –Low classification error rates –Robust in noisy environments –Rules may be more concise if strong relationships in attributes –Provides high degree of accuracy Disadvantages –Multiple passes over database - Very expensive –Classification process not apparent - “Black Box” Embedded within graph structure and link weights May be difficult to generate rules Difficult to infuse domain knowledge in neural net –May have overfitting –May fail to converge Neural Nets Advantages/Disadvantages

19. CSE 8392 Spring 199969 Cluster Enumerate ruleset for network outputs Enumerate ruleset for network inputs Merge input and output rulesets Algorithm p958 Neural Net Mining Example –Lu, section 2.2 Rule Extraction (RX) Algorithm (R[4])

20. CSE 8392 Spring 199970 Network training time Possible methods for incremental training of network Use of domain experts Reduction of inputs to network Neural Net Data Mining Research Issues

21. CSE 8392 Spring 199971 In Bayesian statistics, we measure the likelihood of observed data y given each value of x, e.g. f ( y | x ) Data mining goal: find the most probable set of class descriptions NASA AutoClass(Fayyad,Ch6) –Discover automatic classifications in data –Like clustering –No prior definition of classes –Classes may be unknown to experts Bayesian Classification (Fayyad,Ch6)

22. CSE 8392 Spring 199972 AutoClass Records represented as vectors of values pdf: Gives “probability of observing an instance possessing any particular attribute value vector.” (p156,Cheeseman) Model: finite mixture distribution Interclass Mixture Probability P = (Xi  Cj | V c, T c, S, I) Two levels of search: maximum posterior parameter (MAP), most probable density function Each model a product of independent probability distributions of attribute subsets

23. CSE 8392 Spring 199973 AutoClass Case Studies IRAS: Spectra Classification –“Minimum” information vs. full access –“Good” outputs are domain specific DNA Codes –Results may extend beyond limits of database LandSat Pixels –Parallelization –Undocumented preprocessing

24. CSE 8392 Spring 199974 Interaction between domain experts and machine –Essential for good results –Experts and machine each have unique strengths –Iterative process Ockham Factor –If a particular parameter does not noticeably improve model, reject models with this parameter AutoClass Issues

25. CSE 8392 Spring 199975 Classification Summary Watch out for preprocessing: key words: –Calibration –Corrected –Averaged –Normalized –Adjusted –Compressed –Subsets –Partitioned –Representative Uncover biases in data collection Engage in full partnership with experts and obtain domain specific results!