1. 1 Knowledge Discovery from DataBases (KDD) A.K.A. Data Mining & by other names as well Carlo Zaniolo UCLA CS Dept

2. 2 What is Data Mining? zData mining yExtraction of interesting ( non-trivial, implicit, previously unknown & potentially useful) patterns or knowledge from huge amount of data. zAlternative names yKnowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence,...

3. 3 Why Data Mining?  Explosive growth of data available—the Big-Data Revolution xBusiness: Web, e-commerce, transactions, stocks, … xScience: Remote sensing, bioinformatics, scientific simulation, … xSociety and everyone: news, digital cameras,...  We are drowning in data -- but starving for knowledge ! yKnowledge is the key to improve your business and operations yData Mining tools and techniques: automate knowledge discovery from large data sets

4. 4 DM Applications E.g.: Marketing products to customers: 1.Find clusters of customers who share the same characteristics: interest, income level, spending habits, etc., 2.Determine customer purchasing patterns over time 3.Cross-market analysis—Find associations/co- relations between product sales (and predict on that basis) 4.Profiling—What types of customers buy what products.

5. 5 DM Applications: Fraud Detection and Security zApproaches: Clustering & outlier detection, looking for unusual patterns. zApplications: Health care, retail, credit card service, telecomm. yAuto insurance: ring of collisions yMoney laundering: suspicious monetary transactions yMedical insurance xProfessional patients, ring of doctors, and ring of references xUnnecessary or correlated screening tests yTelecommunications: phone-call fraud xPhone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm yAnti-terrorism

6. 6 New Applications zSoftware Bug Mining zGraph Mining: e.g. finding social networks zWeb Mining zPersonalization and reccomendations zMining and Scientific Applications—Biology zSpatio-Temporal and GIS: yFind geographical clusters. yMine for trajectories and travel plans. zMulti Relational Data Mining yMining for knowledge and relationship from multiple tables, as in yInductive Logic Programming.

7. 7 New Research Topics zTheoretical foundations zStatistical Data Mining zVisual Data Mining zPrivacy-Preserving Data Mining

8. 8 A Historical Perspective 1. Machine Learning (AI) 2. Decision Support Environments: Scalability, Integration, Warehousing, OLAP (DB) 3.Statistical foundation and synergism with other disciplines—e.g., visualization. 4.Mining Streams of sensor & web data

9. 9 Work plan zIntroduction Core Techniques: 1. Classification, 2. Association, and 3. Clustering zProcess and Systems zNew Applications and Research Directions

10. 10 Knowledge Discovery (KDD) Process yData mining—core of knowledge discovery process Data Cleaning Data Integration Data Warehouse Task-Specific Data Data Selection & preprocessing Data Mining Pattern& Rules Useful New knowledge Data Sources: transactional & operational data Auditing