1. CSE/CBS 572: Data Mining by Huan Liu
Huan Liu, CSE, CEAS, ASU
Agenda
Self introduction of all
What’s the course
Questions/Answers
Organization
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

2. CSE/CBS 572: Data Mining by Huan Liu
Contents of basic and advanced topics
Classification, Clustering, Association, and Applications
Format – An interactive and hands-on course with ample opportunities to work, create and share
Paper reading, discussion, project, presentation, or any learning activities you can suggest
Assessment in various ways
Class participation, assignments, quizzes, a course project, presentations, 1 or 2 exams
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

3. CSE/CBS 572: Data Mining by Huan Liu
You: our future data mining star, a potential zillionaire
TA: Lei Tang,
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, make sure won’t miss important announcement and your address stays current (I push some information to you via s sometimes)
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

4. CSE/CBS 572: Data Mining by Huan 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.
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

5. Point distribution (tentative)
Projects (20-25%)
Reading/presentation assignment (5%)
Exam(s) (50%)
Assignments (20-25%), and class participation, quizzes (up to 10% extra credit)
Late penalty, YES, increasing exponentially.
Academic integrity (
4/9/2019
CSE/CBS 572: Data Mining by Huan 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 – part of the learning process.
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 Feburary, if possible.
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

7. CSE/CBS 572: Data Mining by Huan Liu
Project
Proposal
A theme-based project for all in class?
More discussion later
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 (so you’re bold and creative) to expand your knowledge in creating something useful and interesting
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

8. Topic Distribution (tentative)
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CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

10. Your first assignment is to think
Think about what you want to accomplish in this course.
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), and briefly justify your choices.
Pick an area of interests and choose a general topic for paper presentation.
Submission is in hardcopy
Complete the above and submit it in the 3rd class (Monday, January 29, 2007).
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CSE/CBS 572: Data Mining by Huan Liu

11. Your 2nd homework (project related) assignment
Find some (2-3) Web 2.0 or HealthCare applications
Wikipedia, some examples: facebook, del.icio.us,myspace, digg, …
Massive data of various types in a hospital environment
Surf the Web and compile their URLs and functions
Choose your favorite one and explain why
How can data mining help in your perspective with your limited knowledge of data mining
Test-drive it (manually, imagine that you have what you wish for, …)
Write a summary about your experience, challenges you encountered, and suggestions if any
Due on Wednesday February 7, 2007
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CSE/CBS 572: Data Mining by Huan Liu

12. Selecting a paper of your interest
First, continue from your 1st (and/or 2nd) homework assignments about your category of interest
Second, you may form presentation groups (e.g., 2-3 a group for discussion purposes)
Third, each student picks a paper from the given category and find 1 additional 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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CSE/CBS 572: Data Mining by Huan Liu

13. CSE/CBS 572: Data Mining by Huan Liu
Introduction
The need for data mining in the Internet era
Data everywhere, examples?
Data mining
Text mining
Image mining
Web mining (log, link, content, blog)
Bioinformatics
Many products and abundant applications
Where do we stand
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CSE/CBS 572: Data Mining by Huan Liu

14. CSE/CBS 572: Data Mining by Huan 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.
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

15. CSE/CBS 572: Data Mining by Huan Liu
Patterns (1)
Patterns are the relationships and summaries derived through a data mining exercise.
Patterns must be:
valid
novel
potentially useful, or actionable
understandable
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CSE/CBS 572: Data Mining by Huan Liu

16. CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

17. CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

18. CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

19. CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

20. CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

21. CSE/CBS 572: Data Mining by Huan Liu
Why data mining
Rapid computerization of businesses produces huge amounts of data
How to make best use of data to our advantages?
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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CSE/CBS 572: Data Mining by Huan Liu

22. CSE/CBS 572: Data Mining by Huan 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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CSE/CBS 572: Data Mining by Huan Liu

23. 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
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

24. CSE/CBS 572: Data Mining by Huan Liu
DM fields
Data mining is an emerging multi-disciplinary field:
Statistics
Machine learning
Databases
Visualization
OLAP and data warehousing
High-performance computing
...
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

25. CSE/CBS 572: Data Mining by Huan Liu
Summary
What is data mining?
KDD - knowledge discovery in databases: non-trivial extraction of implicit, previously unknown and potentially useful/actionable 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 …
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

26. An Overview of KDD Process (Guess which is which)
Various databases
Integration
Datawarehouse
Preprocessing
Data mining
Post processing
Knowledge
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

27. Web mining – an application
The Web is a massive database
Semi-structured data
XML and RDF
Web mining
Content
Structure
Usage
Link analysis
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu

28. CSE/CBS 572: Data Mining by Huan Liu
About project
Data collection
How, where, what to collect
Evaluation methods
How and why
Analysis and mining
What requires our (yet-to-develop) expertise
For what
Potentially useful methods
For example, graph theories, social networks
Great applications
4/9/2019
CSE/CBS 572: Data Mining by Huan Liu