2. An Overview of Our Course: CS512@2015
4/17/2017
An Overview of Our Course:
Jiawei Han
Department of Computer Science
University of Illinois at Urbana-Champaign
April 17, 2017

3. Data and Information Systems (DAIS:) Course Structures at CS/UIUC
4/17/2017
Data and Information Systems (DAIS:) Course Structures at CS/UIUC
Three main streams: Database, data mining and text information systems
Seminar: Yahoo!-DAIS Seminar: (CS591DAIS—Fall+Spring)
Database Systems:
Database management systems (CS411: Fall+Spring)
Advanced database systems (CS511: Fall)
Human-in-the-loop Data Management (CS 598: Aditya Parameswaran)
Data mining
Intro. to data mining (CS412: Fall)
Data mining: Principles and algorithms (CS512: Spring (Han))
Seminar: Advanced Topics in Data mining (CS591Han—Fall+Spring)
Text information systems
Introduction to Text Information Systems (CS410: Spring (Zhai))
Advance Topics on Information Retrieval (CS 598: Fall (Zhai))
Social and Economic Networks
Social and Economic Networks (CS 598: Hari Sundaram) 3

4. Coursera Data Mining Sepcialization
4/17/2017
Coursera Data Mining Sepcialization
Course 1: Pattern Discovery in Data Mining (Feb. 9, 2015, for four weeks): Jiawei Han
Course 2: Text Retrieval and Search Engines (March 14, 2015): ChengXiang Zhai
Course 3: Cluster Analysis in Data Mining (April 27, 2015): Jiawei Han
Course 4: Text Mining and Analytics (June 8, 2015): ChengXiang Zhai
Course 5: Data Visualization (July 20, 2015): John Hart
Course 6: Capstone Data Mining Capstone (Aug. 31, 2015 for 6 weeks) 4

5. 4/17/2017
Topic Coverage:
Background: CS412: Chaps of Han, Kamber, Pei: “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 3rd ed. 2011
Introduction to networks (ref: class-notes, Newman: 2010 textbook)
Mining information networks (ref: Sun+Han, e-book, 2012, research papers + slides)
Construction of heterogeneous info. networks from text-rich, noisy data
Advanced clustering and outlier analysis (Chaps Han, Kamber, Pei: “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2011
Mining data streams (ref. 2nd ed. Textbook (BK2): Chap. 8)
Spatiotemporal and mobility data mining (ref: BK2: Chap. 10) 5

6. Class Information Instructor: Jiawei Han (www.cs.uiuc.edu/~hanj)
4/17/2017
Class Information
Instructor: Jiawei Han (
Lectures: Tues/Thurs 9:30-10:45am (0216 Siebel Center)
Office hours: Tues/Thurs. 10:45-11:30am (2132 SC)
Teach Assistants:
Hao Luo, Honglei Zhuang, Chao Zhang
Prerequisites (course preparation)
CS412 (offered every Fall) or consent of instructor
General background: Knowledge on statistics, machine learning, and data and information systems will help understand the course materials
Course website (bookmark it since it will be used frequently!)
Textbook:
Yizhou Sun and Jiawei Han, Mining Heterogeneous Information Networks: Principles and Methodologies, Morgan & Claypool, 2012
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011
A set of recent published research papers (see course syllabus) 6

7. Textbook & Recommended Reference Books
4/17/2017
Textbook & Recommended Reference Books
Textbook
Yizhou Sun and Jiawei Han, Mining Heterogeneous Information Networks: Principles and Methodologies, Morgan & Claypool, 2012
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011
Recommended reference books
M. Newman, Networks: An Introduction, Oxford Univ. Press, 2010.
D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge Univ. Press, 2010.
P. S. Yu, J. Han, and C. Faloutsos (eds.), Link Mining: Models, Algorithms, and Applications, Springer, 2010.
M. J. Zaki and W. Meira, Jr. “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Cambridge University Press, 2014
K. P. Murphy, "Machine Learning: a Probabilistic Perspective", MIT Press, 2012. 7

8. Course Work: Assignments, Exam and Course Project
4/17/2017
Course Work: Assignments, Exam and Course Project
Assignments: 10% (2 assignments)
Two Midterm exams: 40% in total (20% each)
Survey and research project proposals: 0%
A 1-2 page proposal on survey + research project will be due at the end of 4th week
Research project midterm reports: 0%
A 4-page project midterm report will be due at the end of 8th week
Survey report: 20%  [no page limit, but expect to be comprehensive and in high quality]
Encourage to align up with your research project topic domain
Hand-in together with companion presentation slides [due at the end of 12th week]
May use 15 min. class survey presentation to replace the report (consent of instructor) — contents must closely aligned with the class content and in very high technical quality
Final course project: 30% (due at the end of semester) 8

9. 4/17/2017
Survey Topics
To be published at our book wiki website as a psedo-textbook/notes
Stream data mining
Sequential pattern mining, sequence classification and clustering
Time-series analysis, regression and trend analysis
Biological sequence analysis and biological data mining
Graph pattern mining, graph classification and clustering
Social network analysis
Information network analysis
Spatial, spatiotemporal and moving object data mining
Multimedia data mining
Web mining
Text mining
Mining computer systems and sensor networks
Mining software programs
Statistical data mining methods
Other possible topics, which needs to get consent of instructor 9

10. 4/17/2017
Research Projects
Final course project: 30% (due at the end of semester)
The final project will be evaluated based on (1) technical innovation, (2) thoroughness of the work, and (3) clarity of presentation
The final project will need to hand in: (1) project report (length will be similar to a typical 8-12 page double-column conference paper), and (2) project presentation slides (which is required for both online and on- campus students)
Each course project for every on-campus student will be evaluated collectively by instructor (plus TA) and other on-campus students in the same class
The course project for online students will be evaluated by instructors and TA only
Group projects (both survey and research): Single-person project is OK, also encouraged to have two as a group, and team up with other senior graduate students, and will be judged by them 10

12. 4/17/2017
Reference Papers
Course research papers: Check reading list and list of papers at the end of each set of chapter slides
Major conference proceedings that will be used
DM conferences: ACM SIGKDD (KDD), ICDM (IEEE, Int. Conf. Data Mining), SDM (SIAM Data Mining), PKDD (Principles KDD)/ECML, PAKDD (Pacific-Asia)
DB conferences: ACM SIGMOD, VLDB, ICDE
ML conferences: NIPS, ICML
IR conferences: SIGIR, CIKM
Web conferences: WWW, WSDM
Social network confs: ASONAM
Other related conferences and journals
IEEE TKDE, ACM TKDD, DMKD, ML,
Use course Web page, DBLP, Google Scholar, Citeseer 12