1. Data Mining: Knowledge Discovery in Databases Peter van der Putten ALP Group, LIACS Pre-University College LAPP-Top Computer Science February 2005

2. Topics Lecture Demo Data Mining tool Exercises Data Mining tool Breaks TBD

3. Data mining case study DARPA’s Bio-surveillance Agent

5. All applications Expert knowledge 29.8% accepted 12.7% infection 34.5% accepted Prediction model plus rules 9.1% infection Accepted volume Data mining case study Credit Scoring for Loan Acceptance © Chordiant Software

6. Data mining case study Classifying Leukemia Problem: –Leukemia (different types of Leukemia cells look very similar) –Given data for a number of samples (patients), can we Accurately diagnose the disease? Predict outcome for given treatment? Recommend best treatment? Solution –Data mining on micro-array data

7. Data mining case study Classifying Leukemia 38 training patients, 34 test patients, ~ 7,000 patient attributes (microarry gene data) 2 Classes: Acute Lymphoblastic Leukemia (ALL) vs Acute Myeloid Leukemia (AML) Use train data to build diagnostic model ALLAML Results on test data: 33/34 correct, 1 error may be mislabeled

8. 5 million terabytes created in 2002 UC Berkeley 2003 estimate: 5 exabytes (5 million terabytes) of new data was created in 2002. Twice as much information was created in 2002 as in 1999 (~30% growth rate) Other growth rate estimates even higher Very little data will ever be looked at by a human Knowledge Discovery is NEEDED to make sense and use of data.

9. Dilbert puts data mining in perspective

10. Sources of (artificial) intelligence Reasoning versus learning Learning from data –Patient data –Customer records –Stock prices –Piano music –Criminal mugshots –Websites –Robot perceptions –Etc.

11. Some working definitions…. ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably Data mining = –The process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data Multidisciplinary field originating from artificial intelligence, pattern recognition, statistics, machine learning, bioinformatics, econometrics, ….

12. Some working definitions…. Concepts: kinds of things that can be learned –Aim: intelligible and operational concept description –Example: the relation between patient characteristics and the probability to be diabetic Instances: the individual, independent examples of a concept –Example: a patient, candidate drug etc. Attributes: measuring aspects of an instance –Example: age, weight, lab tests, microarray data etc Pattern or attribute space

13. Data mining tasks Predictive data mining –Classification: classify an instance into a category –Regression: estimate some continuous value Descriptive data mining –Matching & search: finding instances similar to x –Clustering: discovering groups of similar instances –Association rule extraction: if a & b then c –Summarization: summarizing group descriptions –Link detection: finding relationships –…

14. Data Mining Tasks: Search f.e. agef.e. weight Finding best matching instances Every instance is a point in pattern space. Attributes are the dimension of an instance, f.e. Age, weight, gender etc. Pattern spaces may be high dimensional (10 to thousands of dimensions)

15. Data Mining Tasks: Classification ageweight Goal classifier is to seperate classes on the basis of known attributes The classifier can be applied to an instance with unknow class For instance, classes are healthy (circle) and sick (square); attributes are age and weight

16. Data Mining Tasks: Clustering f.e. agef.e. weight Clustering is the discovery of groups in a set of instances Groups are different, instances in a group are similar In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user

17. Data Mining Tasks: Clustering f.e. agef.e. weight Clustering is the discovery of groups in a set of instances Groups are different, instances in a group are similar In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user In >3 dimensions this is not possible

18. Examples of Classification Techniques Majority class vote Machine learning & AI Decision trees Nearest neighbor Neural networks Genetic algorithms / evolutionairy computing Artificial Immune Systems Good old statistics …..

20. Example Classification Algorithm 1 Decision Trees 20000 patients age > 67 18800 patients gender = male? 1200 patients Weight > 85kg 800 customers Diabetic (%10) etc. 400 patients Diabetic (%50) no yes no

21. Decision Trees in Pattern Space ageweight Goal classifier is to seperate classes (circle, square) on the basis of attribute age and income Each line corresponds to a split in the tree Decision areas are ‘tiles’ in pattern space

22. Decision Trees in Pattern Space ageweight Goal classifier is to seperate classes (circle, square) on the basis of attribute age and income Each line corresponds to a split in the tree Decision areas are ‘tiles’ in pattern space

23. Special Cases of Decision Trees Depth = 0 –Majority class classifier (ZeroR) Depth = 1 –One question only –Also known as decision stump Depth = n –Any amount of branches Various algorithms exist to learn the tree from data –Major difference is criterion to determine on what attribute value to split

24. Example classification algorithm 2: Nearest Neighbour Data itself is the classification model, so no abstraction like a tree etc. For a given instance x, search the k instances that are most similar to x Classify x as the most occurring class for the k most similar instances

25. = new instance Any decision area possible Condition: enough data available Nearest Neighbor in Pattern Space Classification fe agefe weight

26. Nearest Neighbor in Pattern Space Voorspellen f.e. agebvb. weight Any decision area possible Condition: enough data available

27. Example classification algorithm 3: Neural Networks Inspired by neuronal computation in the brain (McCullough & Pitts 1943 (!)) Input (attributes) is coded as activation on the input layer neurons, activation feeds forward through network of weighted links between neurons and causes activations on the output neurons (for instance diabetic yes/no) Algorithm learns to find optimal weight using the training instances and a general learning rule.

28. Example simple network (2 layers) Probability of being diabetic = f (age * weight age + body mass index * weight body mass index) Neural Networks Weight body mass index Probability of being diabetic age body_mass_index weight age

29. Neural Networks in Pattern Space Classification f.e. agef.e. weight Simpel network: only a line available (why?) to seperate classes Multilayer network: Any classification boundary possible

30. e Decision Tree Demo in WEKA, An open source mining tool

31. Descriptive data mining: association rules Discovery of interesting patters Rule format: if A (and B and C etc) then Z Example: –If customer buys potatoes (A) and sauerkraut (B) then customer buys sausage (Z) Quality measures for a rule –Support condition: how often do potatoes and sauerkraut occur together (A,B) –Confidence rule: how often do sausages then occur / support conditions (is A,B  C always true?)

32. e Associatie rule demo in WEKA

33. Some examples of my research areas (Jointly with students) Mix between applications and new algorithms –Video mining: recognize settings, porn filtering –Artificial Immune Systems: copying learning ability of immune systems –Predicting Survival Rate for Throat Cancer Patients –Crime Data Mining –Fusing Data from Multiple Sources –Decisioning: offering the right product to the right customer using predictions –Bias variance evaluation: distinguish between different sources of error for a classifier

34. What have we learned so far? Case Studies Learning versus reasoning Data mining definitions Data mining tasks Example data mining techniques for classification Example data mining techniques for association rules WEKA Demos And now: lab sessions