The document summarizes a data mining program held at Renmin University in Beijing from May 21-27, 2012. It discusses the various lecturers and topics covered during the program. Professors Yang, Han, and Pei each gave lectures on their areas of expertise, including classification and transfer learning, information network models, and mining uncertain data. The curriculum focused mainly on data mining and included both basic and advanced concepts. Participants were encouraged to actively engage and ask questions throughout the program.

1. Summarization for Dragon Star
Program
(Renmin Univ, Beijing, 5.21~5.27, 2012)
Yueshen Xu
xuyueshen@163.com
Zhejiang University
05/28/12 ZJU

2. Overview
 Narration
 What they addressed
 Program Profile
 Knowledge and Expertise
 Argumentation No
 What I think over Dazzle
 Research and Research Mode
 Potpourri
 Discussion
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3. Organizer and Lecturer
 Organizer  Lecturer
• Classification • Network Model
• Transfer • Relationship
An Learning Mining over
amiable DBLP
lady
CuiPing Li Prof. Qiang Yang, HKUST Prof. Jiawei Han, UIUC
• Online Group • Mining on
Behavior over Uncertain
Social Network Data
guest
Prof. Liu Huan, Prof. Jian Pei, SFU
Jun He ASU
appearanc
e
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4. Curriculum
 Contents
 Mainly about Data Mining
 A little about machine learning and database
 Base + Advance
 Base: All should know
 Advance: Only a few know
6:30
 Syllabus
 Tight and tired
 Participation Prof. Liu
 On time, in time and full time
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5. Attention
• No
qualification • What you research is to what you
meet.
 No comment, no guess, just what it’s what
 No topics, no transformation and no speculation
• What they told me are
 No detail, just summarization summarization
 Further study resource repository • Digestitnot too muchit
• Learn for needing
 http://www.cse.ust.hk/~qyang/2012DStar/
 http://www.cs.uiuc.edu/~hanj/dragon12/info12.htm
 Ask for me
 Ask for me all is OK
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6. Prof. Yang
 Classification & Transfer Learning
 Classification Prof. Yang, can
 Decision Trees you speak a little
 Neural Networks faster?
 Replaced by SVM
 Bayesian Classifiers
Just Summarization,
 Conditional Independence
little detail
 Naïve Bayesian Network
 Support Vector Machines
 Little about why, mainly about what
 Ensemble Classifiers
 Bagging and Boost (Ada boost)
 Random Forest
 Collaborative Filtering
 A little
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7. Prof. Yang
 Classification & Transfer Learning
 Transfer Learning
 What he and his students good at and maybe only good at
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8. Prof. Yang
 Classification & Transfer Learning
 I don’t know, but I can bamboozle you
 Transfer Learning
The ability of a system to recognize and apply knowledge and
skills learned in previous tasks to novel tasks or new domains
 Easy to talk, hard to do
05/28/12 ZJU

9. Prof. Yang
 Classification & Transfer Learning
 What they focus on
 Heterogeneous Transfer Learning
 Source-free selection transfer learning
 Multi-task transfer learning
 Transfer Learning for Link Prediction
 EigenTransfer: A Unified Framework for Transfer Learning
05/28/12 ZJU

10. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 An amiable and rigorous old senior
 He is involved in the whole process of each paper, ‘Cause he knows
details well
 He would like to answer every questions
 Never acting superior
 Information Network Model:
 Great powers of conception
 Fundamental theory of network analysis
 Not just about social network. Take a glance at Prof. Han’s contents:
─ Network Science
─ Measure of Metrics of Networks
─ Models of Network Formation
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11. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 Network Science  Plentiful  Models of Network Formation
 Social network  Explain how social networks
 Social network example should be organized
 Friendship networks vs. blogosphere  Model the graph generation
 Other Network process of social networks
 Communication Network  Probabilistic Distribution
 Power Law  Long tail law
 Biological Network
 The Erdös-Rényi (ER) Model
 The Watts and Strogatz Model
Network model and their
representation
Too many, just list some:
• PageRank, Bipartite Networks
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12. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 All based on DBLP
 Why? ‘Cause it’s heterogeneous networks
 Clustering, Ranking in information networks
 Problems  What they mine
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13. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 Classification of information networks
 Is VLDB a conference belonging to DB or DM?
 Similarity Search in information networks
 DBLP
Who are the most similar to “Christos Faloutsos”?
 IMDB
Which movies are the most similar to “Little Miss Sunshine”?
 E-Commerce
Which products are the most similar to “Kindle”?
Y. Sun, J. Han, X. Yan, P. S. Yu, and Tianyi Wu, “PathSim: Meta Path-Based Top-
K Similarity Search in Heterogeneous Information Networks”, VLDB'11
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14. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 What they take advantage of?
 Network Schema, called Meta-Path, take an example:
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15. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 Relationship Prediction in Information Networks
 Whom should I collaborate with?
 Which paper should I cite for this topic?
 Whom else should I follow on Twitter?
Y.Sun, R.Barber, M.Gupta, C.Aggarwal and J.Han. “Co-author Relationship
Prediction in Hererogeneous Bibliographic Networks”, ASONAM’11, July 2011
 Role Discovery: Extraction Semantic Information from
Links
Ref. C. Wang, J. Han, et al., “Mining Advisor-Advisee Relationships from
Research Publication Networks”, SIGKDD 2010
 Data Cleaning and Trust Analysis by InfoNet Analysis
Xiaoxin Yin, Jiawei Han, Philip S. Yu, “Truth Discovery with Multiple Conflicting
Information Providers on the Web”, TKDE’08
05/28/12 ZJU

16. Prof. Han
 Information Network Model & Relationship Mining over DBLP
 Automatic discovery of Entity Pages
 (T. Weinger, Jiawei Han et al. WWW’11)
 Given a reference page, can we find entity pages of the same
Type?
 14 pages references
05/28/12 ZJU

17. Prof. Pei
 Uncertain Data Mining
 Mining uncertain data  Probability is vital
 Models and Representation of uncertain data
 Mining Frequent Patterns
 Classification
 Clustering
 Outlier Detection
 Topic-Oriented
 Nothing to do with database, namely nothing to do with query
 Learn yourself
 Outlier Detection on uncertain data is a challenge
 This is what I most concern about from point view of knowledge
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18. Our Thoughts
 As for pure research, there is no speculation
 What’s the proper mode for research?
 Method-Oriented: Prof. Yang
All about transfer learning
All I have to do is solve practical problems with transfer learning, eg.
Link predication.
 Application-Oriented: Prof. Han
Find fun in DBLP, all about relationship mining
Every part of Prof. Han’s method is not new, but leading by the problem,
the whole framework is innovative
 Topic-Oriented: Prof. Pei
Clustering and outlier detection on uncertain data
He and his team is dependent on solid accumulation
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19. Our Thoughts
Is the problem valuable? Can it
be solved by us?
 How do they do research? Revise many
 Accumulation  Real world problem  Valuable research problem 
times
Discuss and test to find a suitable method  Experiment  Paper
 Accumulated by means of and hard
Experience imitation Test again and again.
work Accumulation, experience,
 Not just scan ppt, but do experiments others had did
judgment….
 Solve problems others had solved
 Different field, different mode
 Application-Oriented: flexible
 Method-Oriented: mathematics
 Topic-Oriented: accumulation
 Work as a Team
05/28/12 ZJU

20. Our Thoughts
 Prof. Pei: Small data
 Can you learn a model just with a little data?
 Data collection is very costly
 Since you can know what you want using 1GB, why do you use
1TB with so many machines?
 Prof. Pei: do we really need experiments? No, provided that what
you have done is really convictive./ Yes, ‘cause our job is not
convictive enough.
 Read every helpful paper
 Research should be labeled by researchers, their teams
and their labs. Everyone has his own pan, not that all
guys just have one.
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21. Our Thoughts
 20/80 Law
 I have fallen behind from others
 I had lost myself in clouds of research for one year. I
hope I can find my way.
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22. Discussion
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