1. CSE591: 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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CSE591: Data Mining by H. Liu

2. CSE591: Data Mining by H. Liu
Contents
Classification, Clustering, Association, and Applications
Format - A seminar course with a lot of assignments and Work
Paper reading, discussion, project, presentation
Assessment
Class participation, assignments, project proposal, presentations, exam(s)
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CSE591: Data Mining by H. Liu

3. CSE591: Data Mining by H. Liu
You
TA: Jigar Mody,
Me: Huan Liu,
Where: Brickyard 566
When: Right after class, other times by appointment
MyASU will be used, so make sure your address is correct & won’t miss important announcement
11/28/2018
CSE591: Data Mining by H. Liu

4. CSE591: Data Mining by H. Liu
Course Format
An experiment since Fall 2000 about effective teaching of graduate data mining
Research papers - the main categories to be found on the course web site
You can choose one of the textbooks listed. A reference list is an entering point for you to access related subjects
Everyone is expected to read the papers and participate in class discussion
Presenters will be evaluated on the spot
11/28/2018
CSE591: Data Mining by H. Liu

5. CSE591: Data Mining by H. Liu
Projects (25%, 10%)
Exam(s) (40%)
Assignment, quizzes and class participation (25%)
Late penalty, YES.
Academic integrity (
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CSE591: Data Mining by H. Liu

6. CSE591: Data Mining by H. Liu
Paper presentation
Each student will be responsible for one topic. All are expected to search for and read the selected material(s) before the presentation.
What is it about?
What are points to discuss and improve?
What can we do with it?
Each presentation is about 30 minutes including discussion, question & answer
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CSE591: Data Mining by H. Liu

7. CSE591: Data Mining by H. Liu
Project
Proposal
Proposal presentation, discussion, revision
A project should be completed in a semester
Project
Presentation and demo
Report
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CSE591: Data Mining by H. Liu

8. Topic Distribution (tentative)
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CSE591: 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)
Selection bias
Learning algorithms
Ensemble methods
Incremental learning
Active learning and co-training
Bioinformatics for CBS 591
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CSE591: Data Mining by H. Liu

10. CSE591: Data Mining by H. Liu
Your first assignment
Think about what you want to accomplish.
List 2 your areas of interests (don’t be restricted by the previous list).
Pick an area of interest and choose a general topic for paper presentation.
Complete the above and submit it in the 2nd class.
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CSE591: Data Mining by H. Liu

11. 2nd Assignment due in two weeks (2/5/04) – due date revised
Choose your category of interest
Find at least 2 quality papers in that category
TA will help you and compile a list of all papers at the end
Write a (< 1 page) summary for each paper
What is it about
Why is it significant and relevant
Where is it published and when
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CSE591: Data Mining by H. Liu

12. CSE591: Data Mining by H. Liu
Introduction
The need for data mining
Data mining
Web mining
Applications
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CSE591: Data Mining by H. Liu

13. CSE591: 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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CSE591: Data Mining by H. Liu

14. CSE591: 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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CSE591: Data Mining by H. Liu

15. CSE591: 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)
etc.
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CSE591: Data Mining by H. Liu

16. CSE591: 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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CSE591: Data Mining by H. Liu

17. CSE591: 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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CSE591: Data Mining by H. Liu

18. CSE591: Data Mining by H. Liu
Some 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 similarity groups in the data
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CSE591: Data Mining by H. Liu

19. CSE591: 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 detection:
discovering the most significant changes in data
Data visualization: using graphical methods to show patterns in data.
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CSE591: Data Mining by H. Liu

20. CSE591: 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.
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CSE591: Data Mining by H. Liu

21. CSE591: Data Mining by H. Liu
Make use 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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CSE591: Data Mining by H. Liu

22. CSE591: Data Mining by H. Liu
Why now
The data is abundant.
The data is being warehoused.
The computing power is affordable.
The competitive pressure is strong.
Data mining tools have become available.
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CSE591: Data Mining by H. Liu

23. CSE591: Data Mining by H. Liu
DM fields
Data mining is an emerging multi-disciplinary field:
Statistics
Machine learning
Databases
Visualization
OLAP and data warehousing
...
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CSE591: Data Mining by H. Liu

24. CSE591: 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 lean - actionability ...
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CSE591: 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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CSE591: 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
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CSE591: Data Mining by H. Liu