1. Copyright R. Weber Machine Learning, Data Mining ISYS370 Dr. R. Weber

2. Copyright R. Weber The game How did you reason to find the rule? According to Michalski (1983) A theory and methodology of inductive learning. In Machine Learning, chapter 4, “inductive learning is a heuristic search through a space of symbolic descriptions (i.e., generalizations) generated by the application of rules to training instances.”

3. Copyright R. Weber Learning Rote Learning –Learn multiplication tables Supervised Learning –Examples are used to help a program identify a concept –Examples are typically represented with attribute-value pairs –Notion of supervision originates from guidance from examples Unsupervised Learning –Human efforts at scientific discovery, theory formation

4. Copyright R. Weber Inductive Learning Learning by generalization Performance of classification tasks –Also categorization Rules indicate categories Goal: –Characterize a concept

5. Copyright R. Weber Learner uses: –positive examples (instances ARE examples of a concept) and –negative examples (instances ARE NOT examples of a concept) Concept Learning is a Form of Inductive Learning

6. Copyright R. Weber Needs empirical validation Dense or sparse data determine quality of different methods Concept Learning

7. Copyright R. Weber The learned concept should be able to correctly classify new instances of the concept –When it succeeds in a real instance of the concept it finds true positives – When it fails in a real instance of the concept it finds false negatives Validation of Concept Learning i

8. Copyright R. Weber The learned concept should be able to correctly classify new instances of the concept –When it succeeds in a counterexample it finds true negatives –When it fails in a counterexample it finds false positives Validation of Concept Learning ii

9. Copyright R. Weber Rule Learning Learning widely used in data mining Version Space Learning is a search method to learn rules Decision Trees

10. Copyright R. Weber Version Space ii Creates tree that includes all possible combinations Does not learn for rules with disjunctions Incremental method, trains additional data without the need to retrain all data A=1 B=0 B=1 B=.5 C=.5C=.3C=1 C=.5 C=.3 C=1 C=.5 C=.3 C=1 Outcome ?

11. Copyright R. Weber Decision trees Knowledge representation formalism Represent mutually exclusive rules (disjunction) A way of breaking up a data set into classes or categories Classification rules that determine, for each instance with attribute values, whether it belongs to one or another class Not incremental

12. Decision trees consist of: - leaf nodes (classes) - decision nodes (tests on attribute values) - from decision nodes branches grow for each possible outcome of the test From Cawsey, 1997

13. Copyright R. Weber Decision tree induction Goal is to correctly classify all example data Several algorithms to induce decision trees: ID3 (Quinlan 1979), CLS, ACLS, ASSISTANT, IND, C4.5 Constructs decision tree from past data Attempts to find the simplest tree (not guaranteed because it is based on heuristics)

14. Copyright R. Weber From: – a set of target classes –Training data containing objects of more than one class ID3 uses test to refine the training data set into subsets that contain objects of only one class each Choosing the right test is the key ID3 algorithm

15. Copyright R. Weber Information gain or ‘minimum entropy’ Maximizing information gain corresponds to minimizing entropy Predictive features (good indicators of the outcome) How does ID3 chooses tests

16. Copyright R. Weber Information gain or ‘minimum entropy’ Maximizing information gain corresponds to minimizing entropy Predictive features (good indicators of the outcome) Choosing tests

17. Copyright R. Weber Clustering Data analysis method applied to data Data should naturally possess groupings Goal: group data into clusters Resulting clusters are collections where objects within a cluster are similar to each other Objects outside the cluster are dissimilar to objects inside Objects from one cluster are dissimilar to objects in other clusters Distance measures are used to compute similarity http://movielens.umn.edu/main

18. Copyright R. Weber Explanation-based learning Incorporates domain knowledge into the learning process Feature values are assigned a relevance factor if their values are consistent with domain knowledge Features that are assigned relevance factors are considered in the learning process

19. Copyright R. Weber Familiar Learning Task Learn relative importance of features Goal: learn individual weights Commonly used in case-based reasoning Methods include a similarity measure to get feedback about verify their relative importance: feedback methods Search methods: gradient descent ID3

20. Copyright R. Weber Esteem CBR shell has option to use Gradient Descent to learn weights:

21. Copyright R. Weber Link analysis Deviation detection Data mining tasks ii Rules: Association generation Relationships between entities How things change over time, trends

22. Copyright R. Weber KDD applications Fraud detection –Telecom (calling cards, cell phones) –Credit cards –Health insurance Loan approval Investment analysis Marketing and sales data analysis Identify potential customers Effectiveness of sales campaign Store layout

23. Copyright R. Weber Text mining The problem starts with a query and the solution is a set of information (e.g., patterns, connections, profiles, trends) contained in several different texts that are potentially relevant to the initial query.

24. Copyright R. Weber Text mining applications IBM Text Navigator –Cluster documents by content; –Each document is annotated by the 2 most frequently used words in the cluster; Concept Extraction (Los Alamos) –Text analysis of medical records; –Uses a clustering approach based on trigram representation; –Documents in vectors, cosine for comparison;

25. rule-based ES case-based reasoning inductive ML, NN algorithms deductive reasoning analogical reasoning inductive reasoning search Problem-solving method Reasoning type