1. CS570: Data Mining Spring 2010, TT 1 – 2:15pm Li Xiong

2. October 15, 2010 Data Mining: Concepts and Techniques 2 Data Mining – What and Why What is data mining Extraction of interesting (non- trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Knowledge mining Why data mining We are drowning in data, but starving for knowledge!

3. October 15, 2010 Data Mining: Concepts and Techniques 3 Knowledge Discovery (KDD) Process Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection and transformation Data Mining Pattern Evaluation

4. October 15, 2010 Data Mining: Concepts and Techniques 4 Evolution of Data and Information Science 1960s: Data collection, database creation, network DBMS 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s: Data mining, data warehousing, multimedia databases, and Web databases 2000s Stream data management and mining Data mining and its applications Web technology (XML, data integration) and global information systems

5. October 15, 2010 Data Mining: Concepts and Techniques 5 Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Other Disciplines Visualization Artificial Intelligence

6. October 15, 2010 Data Mining: Concepts and Techniques 6 Multi-Dimensional View of Data Mining Data View: Data to be mined Relational, data warehouse, transactional, stream, object- oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Knowledge View: Knowledge to be mined Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Method View: Techniques utilized Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Application View: Applications adapted Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.

7. Course Topics Basic Data preprocessing Frequent pattern mining and association analysis Classification and prediction Cluster analysis Applications and emerging topics

8. October 15, 2010 Data Mining: Concepts and Techniques 8 Data Mining – Frequent patterns mining and association analysis Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set Frequent sequential pattern Frequent structured pattern Applications Basket data analysis: what products were often purchased together?— Beer and diapers?! Web log (click stream) analysis DNA sequence analysis Topics Algorithms: Apriori, Frequent pattern growth, Vertical format Closed and maximal patterns Association rules mining

9. October 15, 2010 Data Mining: Concepts and Techniques 9 Classification and Prediction Multidimensional concept description: Characterization and discrimination Big Spenders vs. budget spenders Classification and prediction Construct models (functions) that describe and distinguish classes or concepts for future prediction E.g., classify customers to big spenders or budget spenders Predict some unknown or missing numerical values

10. October 15, 2010 Data Mining: Concepts and Techniques 10 Cluster Analysis Cluster analysis Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier analysis Outlier: Data object that does not comply with the general behavior of the data Noise or exception? Useful in fraud detection, rare events analysis E.g. Extreme large purchase Topics K-means clustering, hierarchical clustering, density based clustering Cluster evaluation

11. Course Topics Basic topics Applications and Emerging Topics Mining data streams and time-series data Graph mining - Social network analysis, link mining Collaborative filtering and recommender systems Biomedical data mining Privacy preserving data mining

12. Privacy Preserving Data Mining Data privacy is a big concern AOL querylog Netflix challenge

13. A Face is exposed for AOL searcher No. 4417749 20 million Web search queries by AOL (650k~ users) User 4417749 “numb fingers”, “60 single men” “dog that urinates on everything” “landscapers in Lilburn, Ga” Several people names with last name Arnold “homes sold in shadow lake subdivision gwinnett county georgia” Thelma Arnold, a 62-year-old widow who lives in Lilburn, Ga., frequently researches her friends’ medical ailments and loves her dogs

14. Assured Information Management and Sharing (AIMS) Health Information De-identification Privacy preserving data publishing and data mining Secure information integration More information http://www.mathcs.emory.edu/aims