Knowledge Discovery in Data [and Data Mining] (KDD)

1. Knowledge Discovery in Data [and Data Mining] (KDD) • Definition := “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad) • Frequently, the term data mining is used to refer to KDD. • Many commercial and experimental tools and tool suites are available (see http://www.kdnuggets.com/siftware.html) • Field is more dominated by industry than by research institutions Let us find something interesting!

2. Making Sense of Data ---Knowledge Discovery and Data Mining • Introduction to KDD (1 class) • Data Warehouses and OLAP (2 classes) • Association Rule Mining (1.5 classes) • Learning to Classify (1 class) • Other Techniques: Clustering, Deviation Detection, Sequential Pattern Analysis (0.5 classes)

3. Data Mining: Confluence of Multiple Disciplines Database Technology Statistics Data Mining Machine Learning Visualization Information Science Other Disciplines

4. General KDD Steps Data sources Selected/Preprocessed data Transformed data Extracted information Knowledge Select/preprocess Transform Data mine Interpret/Evaluate/Assimilate Data preparation

5. Popular KDD-Tasks • Classification (try to learn how to classify) • Clustering (finding groups of similar object) • Estimation and Prediction (try to learn a function that predicts an th value of a continuous output variable based on a set of input variables) • Bayesian and Dependency Networks • Deviation and Fraud Detection • Text Mining • Web Mining • Visualization • Transformation and Data Cleaning

6. KDD and Classical Data Analysis • KDD is less focused than data analysis in that it looks for interesting patterns in data; classical data analysis centers on analyzing particular relationships in data. The notion of interestingness is a key concept in KDD. Classical data analysis centers more on generating and testing pre-structured hypothesis with respect to a given sample set. • KDD is more centered on analyzing large volumes of data (many fields, many tuples, many tables, …). • In a nutshell the the KDD-process consists of preprocessing (generating a target data set), data mining (finding something interesting in the data set), and post processing (representing the found pattern in understandable form and evaluated their usefulness in a particular domain); classical data analysis is less concerned with the the preprocessing step. • KDD involves the collaboration between multiple disciplines: namely, statistics, AI, visualization, and databases. • KDD employs non-traditional data analysis techniques (neural networks, association rules, decision trees, fuzzy logic, evolutionary computing,…).

7. Generating Models as an Example • The goal of model generation (sometimes also called predictive data mining) is the creation, evaluation, and use of models to make predictions and to understand the relationships between various variables that are described in a data collection. Typical example application include: • generate a model to that predicts a student’s academic performance based on the applicants data such as the applicant’s past grades, test scores, past degree,… • generate a model that predicts (based on economic data) which stocks to sell, hold, and buy. • generate a model to predict if a patient suffers from a particular disease based on a patient’s medical and other data. • Model generation centers on deriving a function that can predict a variable using the values of other variables: v=f(a1,…,an) • Neural networks, decision trees, naïve Bayesian classifiers and networks, regression analysis and many other statistical techniques, fuzzy logic and neuro-fuzzy systems, association rules are the most popular model generation tools in the KDD area. • All model generation tools and environments employ the basic train-evaluate-predict cycle.

8. Why Do We Need so manyData Mining / Analysis Techniques? • No generally good technique exists. • Different methods make different assumptions with respect to the data set to be analyzed (to be discussed on the next transparency) • Cross fertilization between different methods is desirable and frequently helpful in obtaining a deeper understanding of the analyzed dataset.

9. Motivation: “Necessity is the Mother of Invention” • Data explosion problem • Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories • We are drowning in data, but starving for knowledge! • Solution: Data warehousing and data mining • Data warehousing and on-line analytical processing • Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases

10. Why Data Mining? — Potential Applications • Database analysis and decision support • Market analysis and management • target marketing, customer relation management, market basket analysis, cross selling, market segmentation • Risk analysis and management • Forecasting, customer retention, improved underwriting, quality control, competitive analysis • Fraud detection and management • Other Applications • Text mining (news group, email, documents) and Web analysis. • Intelligent query answering

11. Market Analysis and Management • Where are the data sources for analysis? • Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies • Target marketing • Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. • Determine customer purchasing patterns over time • Conversion of single to a joint bank account: marriage, etc. • Cross-market analysis • Associations/co-relations between product sales • Prediction based on the association information

12. Fraud Detection and Management • Applications • widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. • Approach • use historical data to build models of fraudulent behavior and use data mining to help identify similar instances • Examples • auto insurance: detect a group of people who stage accidents to collect on insurance • money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network) • medical insurance: detect professional patients and ring of doctors and ring of references

13. Other Applications • Sports • IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat • Astronomy • JPL and the Palomar Observatory discovered 22 quasars with the help of data mining • Internet Web Surf-Aid • IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.

14. Data Mining and Business Intelligence Increasing potential to support business decisions End User Making Decisions Business Analyst Data Presentation Visualization Techniques Data Mining Data Analyst Information Discovery Data Exploration Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA DBA Data Sources Paper, Files, Information Providers, Database Systems, OLTP

15. Architecture of a Typical Data Mining System Graphical user interface Pattern evaluation Data mining engine Knowledge-base Database or data warehouse server Filtering Data cleaning & data integration Data Warehouse Databases

16. An OLAM Architecture Mining query Mining result Layer4 User Interface User GUI API OLAM Engine OLAP Engine Layer3 OLAP/OLAM Data Cube API Layer2 MDDB MDDB Meta Data Database API Filtering&Integration Filtering Layer1 Data Repository Data cleaning Data Warehouse Databases Data integration

17. Example: Decision Tree Approach

18. Decision Tree Approach2

19. Decision Trees • Example: • Conducted survey to see what customers were interested in new model car • Want to select customers for advertising campaign training set

20. One Possibility age<30 Y N city=sf car=van Y Y N N likely unlikely likely unlikely

21. Another Possibility car=taurus Y N city=sf age<45 Y Y N N likely unlikely likely unlikely

22. Example: Nearest Neighbor Approach

23. Clustering income education age

24. Another Example: Text • Each document is a vector • e.g., <100110...> contains words 1,4,5,... • Clusters contain “similar” documents • Useful for understanding, searching documents sports international news business

25. Issues • Given desired number of clusters? • Finding “best” clusters • Are clusters semantically meaningful? • e.g., “yuppies’’ cluster? • Using clusters for disk storage

26. Association Rule Mining transaction id products bought customer id sales records: market-basket data • Trend: Products p5, p8 often bough together • Trend: Customer 12 likes product p9

27. Summary Data Mining • Data mining: discovering interesting patterns from large amounts of data • A natural evolution of database technology, in great demand, with wide applications • A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation • Mining can be performed in a variety of information repositories • Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. • Classification of data mining systems • Major issues in data mining

28. Where to Find References? • Data mining and KDD (SIGKDD member CDROM): • Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc. • Journal: Data Mining and Knowledge Discovery • Database field (SIGMOD member CD ROM): • Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAA • Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc. • AI and Machine Learning: • Conference proceedings: Machine learning, AAAI, IJCAI, etc. • Journals: Machine Learning, Artificial Intelligence, etc. • Statistics: • Conference proceedings: Joint Stat. Meeting, etc. • Journals: Annals of statistics, etc. • Visualization: • Conference proceedings: CHI, etc. • Journals: IEEE Trans. visualization and computer graphics, etc.