1. CSE572: Data Mining by H. Liu
Huan Liu, CSE, CEAS, ASU
Agenda
Self introduction of all
What’s the course
Questions/Answers
Organization
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CSE572: Data Mining by H. Liu

2. CSE572: Data Mining by H. Liu
Contents of basic and advanced topics
Classification, Clustering, Association, and Applications
Format – An interactive course with ample opportunities to work, create and share
Paper reading, discussion, project, presentation, or any learning activities you can suggest
Assessment
Class participation, assignments, quizzes, a course project, presentations, 1 or 2 exams
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CSE572: Data Mining by H. Liu

3. CSE572: Data Mining by H. Liu
You: our future successful data miner, a likely a zillionaire
TA: Alan Zheng Zhao,
Me: Huan Liu,
Where: Brickyard 566
When: see on the course website, or by appointment
“No pain, no gain”, or “As you sow, so you shall reap”, we will also learn the principle of “No Free Lunch”.
MyASU will be used, so make sure your address is correct & won’t miss important announcement
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CSE572: Data Mining by H. Liu

4. CSE572: Data Mining by H. Liu
Course Format
What is the effective teaching of graduate data mining ? Your feedback is keenly sought.
Current research papers - the main categories to be found on the course web site.
You can choose one of the textbooks listed. It is an entering point for you to access related subjects. The truth is It is a fast changing field.
Everyone is expected to read research papers and participate in class discussion.
Paper presentations.
Project presentations.
Presentations will also be evaluated in class.
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CSE572: Data Mining by H. Liu

5. Point distribution (tentative)
Projects (35%)
Reading/presentation assignment (10%)
Exam(s) (40%)
Assignments (15%), and class participation, quizzes (up to 10% extra credit)
Late penalty, YES, increased exponentially.
Academic integrity (
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CSE572: Data Mining by H. Liu

6. Research paper reading
We will provide a reading list and you can also choose your favorite
All are expected to search for and read the selected papers.
What is it about (e.g., key idea, basic algorithm)?
What are points to discuss and improve?
What can we do with it?
What to submit? (see more on the class website)
A brief report that describes the above and 2 questions suitable for quizzes/tests with solutions
A set of presentation slides for 20 minutes
Due date: TBA, use digital drop box
Grading criteria include (1) quality of additional papers you select, (2) slides for presentation, (3) the report, and (4) oral presentation will be selected among the best submissions and presenters will be given extra credit based on presentation
Presentation can start as early as in September, if possible.
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CSE572: Data Mining by H. Liu

7. CSE572: Data Mining by H. Liu
Project
Proposal
Proposal presentation, discussion, revision
A project worth the effort of a semester’s work
Progress report
Final report
Class presentation and/or demo
One key goal of this course is to take advantage of your intelligence and (limited) experience to expand your knowledge in creating something useful and interesting
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CSE572: Data Mining by H. Liu

8. Topic Distribution (tentative)
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CSE572: Data Mining by H. Liu

9. Categories of interests (including design and implementation)
Data and application security
Data mining and privacy
Data reduction and selection
Streaming data reduction
Dealing with large data (column- & row-wise)
Search bias, overfitting
Learning algorithms
Ensemble methods
Semi-supervised learning
Active learning and co-training
Bioinformatics or others
A discussion board will be created, if needed
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CSE572: Data Mining by H. Liu

10. Your first assignment – to think
Think about what you want to accomplish.
List 2 of your areas of interests (don’t be restricted by the previous list, and this is one of some rare opportunities that allow you to day-dream and earn grade points).
Pick an area of interests and choose a general topic for paper presentation.
Submission via MyASU or hardcopy
Complete the above and submit it in the 3rd class (next Tuesday Sept 5th).
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CSE572: Data Mining by H. Liu

11. 2nd Assignment due on Sept 13
First, state your category of interest
Second, form presentation groups (3-4 a group)
Third, each student picks a paper from the given category and find 1 high-quality relevant paper
Submit it through myASU
The first student in each category will present the given paper (s/he does not need to look for another paper)
TA will organize and help you and compile a list of all papers at the end
Write a summary for your selected paper including
What is it about
Why is it significant and relevant
Where is it published and when
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CSE572: Data Mining by H. Liu

12. CSE572: Data Mining by H. Liu
Introduction
The need for data mining
Data mining
Text mining
Image mining
Web mining (log, link, content)
Bioinformatics
Many products and abundant applications
Where do we stand
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CSE572: Data Mining by H. Liu

13. CSE572: Data Mining by H. Liu
What is data mining
Data mining is
extraction of useful patterns from data sources, e.g., databases, texts, web, image.
the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
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CSE572: Data Mining by H. Liu

14. CSE572: Data Mining by H. Liu
Patterns (1)
Patterns are the relationships and summaries derived through a data mining exercise.
Patterns must be:
valid
novel
potentially useful
understandable
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CSE572: Data Mining by H. Liu

15. CSE572: Data Mining by H. Liu
Patterns (2)
Patterns are used for
prediction or classification
describing the existing data
segmenting the data (e.g., the market)
profiling the data (e.g., your customers)
Detection (e.g., intrusion, fault, anomaly)
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CSE572: Data Mining by H. Liu

16. CSE572: Data Mining by H. Liu
Data mining typically deals with data that have already been collected for some purpose other than data mining.
Data miners usually have no influence on data collection strategies.
Large bodies of data cause new problems: representation, storage, retrieval, analysis, ...
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CSE572: Data Mining by H. Liu

17. CSE572: Data Mining by H. Liu
Even with a very large data set, we are usually faced with just a sample from the population.
Data exist in many types (continuous, nominal) and forms (credit card usage records, supermarket transactions, government statistics, text, images, medical records, human genome databases, molecular databases).
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CSE572: Data Mining by H. Liu

18. CSE572: Data Mining by H. Liu
Typical DM tasks
Classification:
mining patterns that can classify future data into known classes.
Association rule mining:
mining any rule of the form X  Y, where X and Y are sets of data items.
Clustering:
identifying a set of similar groups in the data
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CSE572: Data Mining by H. Liu

19. CSE572: Data Mining by H. Liu
Sequential pattern mining:
A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidence
Deviation/anomaly/exception detection:
discovering the most significant changes in data
Data visualization (or visual analytics): using graphical methods to show patterns in data.
High performance computing
Bioinformatics
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CSE572: Data Mining by H. Liu

20. CSE572: Data Mining by H. Liu
Why data mining
Rapid computerization of businesses produces huge amounts of data
How to make best use of data?
A growing realization: knowledge discovered from data can be used for competitive advantage and to increase business intelligence.
There are problems that might not be suitable for data mining – Top 10 Statistics Problems for CapitalOne (Bill Khan’s invited talk at SIGKDD’06)
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CSE572: Data Mining by H. Liu

21. CSE572: Data Mining by H. Liu
Make use/sense of your data assets
Many interesting things you want to find cannot be found using database queries
“find me people likely to buy my products”
“Who are likely to respond to my promotion”
Fast identify underlying relationships and respond to emerging opportunities
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CSE572: Data Mining by H. Liu

22. Why now and for the near future
The data is abundant.
The data is being collected or warehoused.
The computing power is affordable.
The competitive pressure is increasing.
Data mining tools have become available.
New challenges
New data types evolve
New applications emerge
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CSE572: Data Mining by H. Liu

23. CSE572: Data Mining by H. Liu
DM fields
Data mining is an emerging multi-disciplinary field:
Statistics
Machine learning
Databases
Visualization
OLAP and data warehousing
High-performance computing
...
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CSE572: Data Mining by H. Liu

24. CSE572: Data Mining by H. Liu
Summary
What is data mining?
KDD - knowledge discovery in databases: non-trivial extraction of implicit, previously unknown and potentially useful information
Why do we need data mining?
Wide use of computer systems - data explosion - knowledge is power – but we’re data rich, knowledge poor – useful, understandable and actionable knowledge ...
Data mining is not a plug-and-play, so we are not done yet and need to continue this class …
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CSE572: Data Mining by H. Liu

25. An Overview of KDD Process (Guess which is which)
Various databases
Integration
Datawarehouse
Preprocessing
Data mining
Post processing
Knolwedge
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CSE572: Data Mining by H. Liu

26. Web mining – an application
The Web is a massive database
Semi-structured data
XML and RDF
Web mining
Content
Structure
Usage
Link analysis
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CSE572: Data Mining by H. Liu