1. Data Mining GyuHyeon Choi

2. ‘80s  When the term began to be used  Within the research community

3. ‘80s  Definition  A set of mechanisms and techniques  To extract hidden information from data  SQL style is not data mining

4. ‘90s  Definition  Sub-process within KDD  (Knowledge Discovery in Databases)  Different with data preparation, analysis, and visualization  Other parts of KDD

5. ‘90s  Became popular significantly  ACM SIGKDD annual conference, 1995  European PKDD conference, 1995  Pacific/Asia PAKDD conference, 1997

6. ‘90s  Contribution of technological advances  Processing power  Data storage capability

7. ‘90s  Processing of large volumes of data  Even using desktop machines  Commercial enterprises started to maintain data  To support commercial activities  But not to mine

8. ‘90s  Large super market chains  Introduction of customer loyalty cards  To record customer purchases  Started mining purchasing patterns

9. Present  Mining non-standard data  Text mining  Image mining  Graph mining

10. Present  Collective term  Different with data preparation, analysis, and visualization  Even called as “big data”

11. Present  Domain of AI and KE  Artificial Intelligence  Knowledge Engineering

12. Present  Application  Rather than a technology

13. Before We Go  Data mining techniques  Pattern extraction  Clustering  Classification

14. Before We Go  Examples of classification

15. Problem  Curse of dimensionality  Data in high-dimensional spaces

16. Problem  Earlier classification (maybe)

17. Problem  Current classification

18. Example  Clustering

19. Example  Anomaly detection

20. Example  Recommender system

21. Problem (again)  Current classification

22. Topic  Complexity  The more complex society  The more complex data mining

23. Dimension Reduction  Use most significant dimensions  Cannot satisfy people’s demand  Waste of storage

24. Principal Component Analysis  Orthogonal transformation  Computationally expensive  Still doubtful to satisfy people’s demand

25. Next Solution?  Currently no solutions  Or no problem

26. Future Solution  No algorithmic solution

27. Future Solution  Advanced hardware  Super-supercomputer

28. Future Problem  Human being as data?  Dimensions of the human Being

29. Future Problem  Can people be satisfied?  More and more sophisticated demand  More and more dimensions of data

30. Frans Coenen, The Knowledge Engineering Review, Vol. 26:1, 25-29, Cambridge University Press, 2011 Curse of dimensionality, Wikipedia Andrew Tarantola, The Quantum D-Wave 2 Is 3,600 Times Faster than a Super Computer, GIZMODO, April 2015 Peter Rüst, Dimensions of the Human Being and of Divine Action, www.asa3.org/ASA/PSCF/, Volume 57, Number 3, September 2005