1. Knowledge Discovery and Data Mining Evgueni Smirnov

2. Outline Data Flood Definition of Knowledge Discovery and Data Mining Possible Tasks: –Classification Task –Regression Task –Clustering Task –Association-Rule Task

3. Data Flood

4. Trends Leading to Data Flood Moore’s law –Computer Speed doubles every 18 months Storage law –total storage doubles every 9 months As a result: More data is captured: –Storage technology faster and cheaper –DBMS capable of handling bigger DB

5. Trends Leading to Data Flood More data is generated: –Business: Supermarket chains Banks, Telecoms, E-commerce, etc. –Web –Science: astronomy, physics, biology, medicine etc.

6. Consequence Very little data will ever be looked at by a human, and thus, we need to automate the process of Knowledge Discovery to make sense and use of data.

7. Definition of Knowledge Discovery Knowledge Discovery in Data is non-trivial process of identifying –valid –novel –potentially useful –and ultimately understandable patterns in data. from Advances in Knowledge Discovery and Data Mining, Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT Press 1996.

8. Related Fields Statistics Machine Learning Databases Visualization Knowledge Discovery

9. Knowledge-Discovery Methodology data Target data Processed data Transformed data Patterns Knowledge Selection Preprocessing & cleaning Transformation & feature selection Data Mining Interpretation Evaluation Data Mining is searching for patterns of interest in a particular representation.

10. Data-Mining Tasks Classification Task Regression Task Clustering Task Association-Rule Task

11. Classification Task Given: a collection of instances (training set) –Each instances is represented by a set of attributes, one of the attributes is the class attribute. Find: a classifier for the class attribute as a function of the values of other attributes. Goal: previously unseen instances should be assigned a class as accurately as possible.

12. Example 1 categorical continuous class Test Set Training Set Classifier Learn Classifier

13. Example 2 Fraud Detection –Goal: Predict fraudulent cases in credit card transactions. –Approach: Use credit card transactions and the information on its account-holder as attributes. –When does a customer buy, what does he buy, how often he pays on time, etc Label past transactions as fraud or fair transactions. This forms the class attribute. Learn a model for the class of the transactions. Use this model to detect fraud by observing credit card transactions on an account.

14. Regression Task Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Examples: Predicting sales amounts of new product based on advertising expenditure. Predicting wind velocities as a function of temperature, humidity, air pressure, etc. Time series prediction of stock market indices.

15. Clustering Task Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that: –Data points in one cluster are more similar; –Data points in separate clusters are less similar. Intra-cluster distances are minimized Intra-cluster distances are minimized Inter-cluster distances are maximized Inter-cluster distances are maximized

16. Example Market Segmentation: –Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. –Approach: Collect different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters.

17. Association-Rule Task Given a set of records each of which contain some number of items from a given collection; –Produce dependency rules which will predict occurrence of an item based on occurrences of other items. Rules Discovered: Milk --> Coke Diaper, Milk --> Beer Rules Discovered: Milk --> Coke Diaper, Milk --> Beer

18. Example Supermarket shelf management. –Goal: To identify items that are bought together by sufficiently many customers. –Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items. –A classic rule -- If a customer buys diaper and milk, then he is very likely to buy beer. So, don’t be surprised if you find six-packs stacked next to diapers!

19. Course Overview data Processed data Selection Preprocessing & cleaning Transformation & feature selection Data Mining Interpretation Evaluation Monday: Decision Trees and Decision Rules (E. Smirnov) Evaluation of learning models (E. Smirnov)

20. Course Overview data Processed data Selection Preprocessing & cleaning Transformation & feature selection Data Mining Interpretation Evaluation Tuesday: Feature selection and extraction (G. Nalbantov) Regression (G. Nalbantov)

21. Course Overview data Processed data Selection Preprocessing & cleaning Transformation & feature selection Data Mining Interpretation Evaluation Wednesday : Instance learning and Bayesian learning (E. Smirnov) Association Rules (E. Smirnov)

22. Course Overview data Processed data Selection Preprocessing & cleaning Transformation & feature selection Data Mining Interpretation Evaluation Thursday: Support Vector Machines (G. Nalbantov)