1. 1. Data Mining (or KDD) Let us find something interesting! Definition := “Data Mining is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad)

2. Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) –remote sensors on a satellite –telescopes scanning the skies –microarrays generating gene expression data –scientific simulations generating terabytes of data –GIS Traditional techniques infeasible for raw data Data mining may help scientists –in classifying and segmenting data –in Hypothesis Formation

3. Ch. Eick: Data Mining 2.1 Supervised Clustering Applications of Supervised Clustering Include: a.Learning Subclasses b.for Region Discovery in Spatial Datasets c.Distance Function Learning d.Data Set Compression (reduce size of dataset by using cluster representatives) e.Adaptive Supervised Clustering Ch. Eick

4. Ch. Eick: Data Mining Example: Finding Subclasses Attribute2 Ford Trucks Attribute1 Ford SUV Ford Vans GMC Trucks GMC Van GMC SUV :Ford :GMC Ch. Eick

5. Ch. Eick: Data Mining SC Algorithms Investigated 1. Representative-based Clustering Algorithms 1.Supervised Partitioning Around Medoids (SPAM). 2.Single Representative Insertion/Deletion Steepest Decent Hill Climbing with Randomized Restart (SRIDHCR). 3.Supervised Clustering using Evolutionary Computing (SCEC) 2. Agglomerative Hierarchical Supervised Clustering (AHSC) 3. Grid-Based Supervised Clustering (GRIDSC) 1.Naïve approach 2.Hierarchical Grid-based Clustering relying on data cubes 3.Grid-based Clustering relying on density estimation techniques

6. Ch. Eick: Data Mining 2.2 Spatial Data Mining (SPDM) SPDM := the process of discovering interesting, useful, non-trivial patterns from (large) spatial datasets. Spatial patterns –Spatial outlier, discontinuities bad traffic sensors on highways – Location prediction models model to identify habitat of endangered species –Spatial clusters crime hot-spots, poverty clusters – Co-location patterns identify arsenic risk zones in Texas and determine if there is a correlation between the arsenic concentrations of the major Texas aquifers and cultural factors such population, farm density and the geology of the aquifers etc. Idea: Reuse the supervised clustering algorithms that already exist by running them with a different fitness function that corresponds to a particular measure of interestingness.

7. Ch. Eick: Data Mining Example: Discovery of “Interesting Regions” in Wyoming Census 2000 Datasets Ch. Eick

8. Ch. Eick: Data Mining 2.3 Distance Function Learning Example: How to Find Similar Patients? Task: Construct a distance function that measures patient similarity Motivation: Finding a “good” distance function is important for: –Case based reasoning –Clustering –Instance-based classification (e.g. nearest neighbor classifiers) Our Approach: Learn distance functions based on training examples and user feedback

9. Ch. Eick: Data Mining The following relation is given (with 10000 tuples): Patient(ssn, weight, height, cancer-sev, eye-color, age,…) Attribute Domains –ssn: 9 digits –weight between 30 and 650; m weight =158 s weight =24.20 –height between 0.30 and 2.20 in meters; m height =1.52 s height =19.2 –cancer-sev: 4=serious 3=quite_serious 2=medium 1=minor –eye-color: {brown, blue, green, grey } –age: between 3 and 100; m age =45 s age =13.2 Task: Define Patient Similarity Motivating Example: How To Find Similar Patients?

10. Ch. Eick: Data Mining Idea: Coevolving Clusters and Distance Functions Clustering X Distance Function  Cluster Goodness of the Distance Function  q(X) Clustering Evaluation Weight Updating Scheme / Search Strategy x x x x o o o o xx o o xx o o o o “Bad” distance function   “Good” distance function   x x o o

11. Ch. Eick: Data Mining Distance Function Learning Framework Randomized Hill Climbing Adaptive Clustering Inside/Outside Weight Updating K-Means Supervised Clustering NN-Classifier Weight-Updating Scheme / Search Strategy Distance Function Evaluation … … Work By Karypis [BECV05] Other Research [ERBV04] Current Research [CHEN05]

12. Ch. Eick: Data Mining Supervised Clustering Algorithm Inputs Clustering Quality Adaptation System Changes Past Experience Feedback Evaluation System Summary Domain Expert q(X), … Fitness Functions (Predefined ) 2.4 Adaptive Data Mining Ch. Eick

13. Ch. Eick: Data Mining 2.5 Signatures of Data Sets Input: a set of classified examples Output: Signatures in the dataset that characterize 1.how the examples of a class distribute (in relationship to the examples of the other classes) in the dataset 2.how many regions dominated by a single class exist in the data set 3.which regions dominated by one class are bordering regions dominated by another class? 4.where are the regions, identified in step 2 and 3, located 5.what are the density attactors (maxima of the density function) of the classes in the data set Why are we creating those signatures? – As a preprocessing step to develop smarter classifiers – To understand why a particular data mining techniques works well / do not work well for a particular dataset  meta learning Methods employed: density estimation techniques, supervised clustering, proximity graphs (e.g. Delaunay, Gabriel graphs),…

14. Ch. Eick: Data Mining Example: Signatures of Data Sets

15. Ch. Eick: Data Mining Attribute 1 Attribute 2 Attribute 1 Attribute 2 Applications of Creating Signatures: Class Decomposition (see also [VAE03]) Attribute 1

16. Ch. Eick: Data Mining 2.6 Research Christoph F. Eick 2005-2007 Measures of Interestingness Clustering for Classification Supervised Clustering Editing / Data Set Compression Spatial Data MiningAdaptive Clustering Distance Function Learning Mining Data Streams Online Data Mining Mining Sensor Data Mining Semi-Structured Data Web Annotation File Prediction Evolutionary Computing Creating Signatures For Datasets

17. Ch. Eick: Data Mining Goal: Development of data analysis and data mining techniques and the application of these techniques to challenging problems in physics, geology, astronomy, environmental sciences, and medicine. Topics investigated: Meta Learning Classification and Learning from Examples Clustering Distance Function Learning Using Reinforcement Learning for Data Mining Spatial Data Mining Knowledge Discovery 3. UH Data Mining and Machine Learning Group (UH-DMML) Co-Directors: Christoph F. Eick and Ricardo Vilalta