9/03 Data Mining – Introduction G Dong (WSU)1 CS499/ Data Mining Fall 2003 Professor Guozhu Dong Computer Science & Engineering WSU

9/03 Data Mining – Introduction Guozhu Dong2 Introduction Introduction to this Course Introduction to Data Mining

9/03 Data Mining – Introduction Guozhu Dong3 Introduction to the Course First, about you - why take this course? Your background and strength AI, DBMS, Statistics, Biology, Business, … Your interests and requests What is this course about? Problem solving Handling data transform data to workable data Mining data turn data to knowledge validation and presentation of knowledge

9/03 Data Mining – Introduction Guozhu Dong4 This course What can you expect from this course? Knowledge and experience about DM Problem solving skills How is this course conducted? Home works, projects, exams, classes Course Format Individual Projects: 30% Exams and/or quizzes: 60% Homeworks: 10%

9/03 Data Mining – Introduction Guozhu Dong5 Course Web Site cs.wright.edu/~gdong/mining03/WSUCS499DataMining.htm My office and office hours RC 430 4:30-5:30, T Th My Slides and relevant information will be made available at the course web site

9/03 Data Mining – Introduction Guozhu Dong6 Any questions and suggestions? Your feedback is most welcome! I need it to adapt the course to your needs. Please feel free to provide yours anytime. Share your questions and concerns with the class – very likely others may have the same. No pain no gain – no magic for data mining. The more you put in, the more you get Your grades are proportional to your efforts.

9/03 Data Mining – Introduction G Dong (WSU)7 Introduction to Data Mining Definitions Motivations of DM Interdisciplinary Links of DM

9/03 Data Mining – Introduction Guozhu Dong8 What is DM? Or more precisely KDD (knowledge discovery from databases)? Many definitions An iterative process, not plug-and-play raw data  transformed data  preprocessed data  data mining  post-processing  knowledge One definition is A non-trivial process of identifying valid, novel, useful and ultimately understandable patterns in data

9/03 Data Mining – Introduction Guozhu Dong9 Need for Data Mining Data accumulate and double every 9 months There is a big gap from stored data to knowledge; and the transition won’t occur automatically. Manual data analysis is not new but a bottleneck Fast developing Computer Science and Engineering generates new demands Seeking knowledge from massive data Any personal experience?

9/03 Data Mining – Introduction Guozhu Dong10 When is DM useful Data rich world Large data (dimensionality and size) Image data (size) Gene chip data (dimensionality) Little knowledge about data (exploratory data analysis) What if we have some knowledge?

9/03 Data Mining – Introduction Guozhu Dong11 DM perspectives KDD “goals”: Prediction, description, explanation, optimization, and exploration Knowledge forms: patterns vs. models Understandability and representation of knowledge Some applications Business intelligence (CRM) Security (Info, Comp Systems, Networks, Data, Privacy) Scientific discovery (bioinformatics, medicine)

9/03 Data Mining – Introduction Guozhu Dong12 Challenges Increasing data dimensionality and data size Various data forms New data types Streaming data, multimedia data Efficient search and access to data/knowledge Intelligent update and integration

9/03 Data Mining – Introduction Guozhu Dong13 Interdisciplinary Links of DM Statistics Databases AI Machine Learning Visualization High Performance Computing supercomputers, distributed/parallel/cluster computing

9/03 Data Mining – Introduction Guozhu Dong14 Statistics  Discovery of structures or patterns in data sets hypothesis testing, parameter estimation Optimal strategies for collecting data  efficient search of large databases Static data  constantly evolving data Models play a central role  algorithms are of a major concern  patterns are sought

9/03 Data Mining – Introduction Guozhu Dong15 Relational Databases A relational database can contain several tables Tables and schemas The goal in data organization is to maintain data and quickly locate the requested data Queries and index structures Query execution and optimization Query optimization is to find the “best” possible evaluation method for a given query Providing fast, reliable access to data for data mining

9/03 Data Mining – Introduction Guozhu Dong16 AI Intelligent agents Perception-Action-Goal-Environment Search Uniform cost and informed search algorithms Knowledge representation FOL, production rules, frames with semantic networks Knowledge acquisition Knowledge maintenance and application

9/03 Data Mining – Introduction Guozhu Dong17 Machine Learning Focusing on complex representations, data-intensive problems, and search-based methods Flexibility with prior knowledge and collected data Generalization from data and empirical validation statistical soundness and computational efficiency constrained by finite computing & data resources Challenges from KDD scaling up, cost info, auto data preprocessing, more knowledge types

9/03 Data Mining – Introduction Guozhu Dong18 Visualization Producing a visual display with insights into the structure of the data with interactive means zoom in/out, rotating, displaying detailed info Various types of visualization methods show summary properties and explore relationships between variables investigate large DBs and convey lots of information analyze data with geographic/spatial location A pre- and post-processing tool for KDD

9/03 Data Mining – Introduction Guozhu Dong19 Bibliography J. Han and M. Kamber. Data Mining – Concepts and Techniques Morgan Kaufmann. D. Hand, H. Mannila, P. Smyth. Principals of Data Mining MIT. W. Klosgen & J.M. Zytkow, edited, 2001, Handbook of Data Mining and Knowledge Discovery.