Download Image-Based Modeling and 5th

Data Mining Techniques
Instructor: Ruoming Jin
Fall 2006
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Welcome!
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Instructor: Ruoming Jin
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Homepage: www.cs.kent.edu/~jin/
Office: 264 MCS Building
Email: [email protected]
Office hour: Mondays and Wednesdays
(10:00AM to 11:00AM) or by appointment
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Overview
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Homepage:
www.cs.kent.edu/~jin/datamining.html
Time: 11:00-12:15PM Monday and
Wednesday
Place: MSB 276
Prerequisite: none
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Preferred: Database, AI, Machine Learning,
Statistics, Algorithms, and Data Structures
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Overview
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Textbook: Introduction to Data Mining – Pang-Ning Tan, Michael
Steinbach, and Vipin Kumar, Addison Wesley
References
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Data Mining --- Concepts and techniques, by Han and Kamber,
Morgan Kaufmann, 2001. (ISBN:1-55860-489-8)
Principles of Data Mining, by Hand, Mannila, and Smyth, MIT
Press, 2001. (ISBN:0-262-08290-X)
The Elements of Statistical Learning --- Data Mining, Inference, and
Prediction, by Hastie, Tibshirani, and Friedman, Springer,
2001. (ISBN:0-387-95284-5)
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Mining the Web --- Discovering Knowledge from Hypertext Data, by
Chakrabarti, Morgan Kaufmann, 2003. (ISBN:1-55860-7544)
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Overview
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Grading scheme
Paper Presentation and
discussion
Project
35%
Attendance and
participation
15%
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No homework
No exam
50%
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Overview (Presentation)
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Paper presentation
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One per student
Research paper(s)
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List of recommendations (will be available by the end
of second week)
Your own pick (upon approval)
Three parts
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Review of research ideas in the paper
Debate (Pros/Cons)
Questions and comments from audience
Class participation: One question/comment per
student
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Overview (Presentation)
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Order of presentation: assigned by instructor
The presentation will start from late October or early
November
You need make your choice and send it to me by Sep.
22nd!
You need submit three drafts before the final presentation
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First draft due on Oct. 14th
Second draft due on Oct. 21th
Final slides due one day before your presentation.
I will provide feedback and suggestion for each draft
Note that I do expect the complete presentation slides in
the first draft
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Overview (Project)
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Project (due Dec 3rd)
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One project: One or Two students
Some suggestion will be available shortly
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Checkpoints
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The project will focus on visualizing data mining algorithm.
Proposal: title and goal (due Oct 7th)
Outline of approach (due Oct 7th)
Implementation (due Dec 3rd)
Evaluation (due Dec 3rd)
Documentation (duce Dec 10rd)
Each group will have a short presentation and demo (20
minutes)
Each group will provide a five-page document on the project
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Topics
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Scope:Data Mining
Topics:
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Association Rule
Sequential Patterns
Graph Mining
Clustering and Outlier Detection
Classification and Prediction
Regression
Pattern Interestingness
Dimensionality Reduction
…
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Topics
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Applications
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Bioinformatics
Web mining
Text mining
Visualization
Financial data analysis
Intrusion detection
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KDD References
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Data mining and KDD (SIGKDD: CDROM)
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Journal: Data Mining and Knowledge Discovery, KDD Explorations
Database systems (SIGMOD: CD ROM)
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Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD,
etc.
Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT,
ICDT, DASFAA
Journals: ACM-TODS, IEEE-TKDE, JIIS, J. ACM, etc.
AI & Machine Learning
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Conferences: Machine learning (ICML), AAAI, IJCAI, COLT (Learning
Theory), etc.
Journals: Machine Learning, Artificial Intelligence, etc.
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KDD References
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Statistics
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Conferences: Joint Stat. Meeting, etc.
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Journals: Annals of statistics, etc.
Bioinformatics
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Conferences: ISMB, RECOMB, PSB, CSB, BIBE, etc.
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Journals: J. of Computational Biology, Bioinformatics, etc.
Visualization
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Conference proceedings: CHI, ACM-SIGGraph, etc.
Journals: IEEE Trans. visualization and computer graphics,
etc.
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