1. University of Economics, Prague MLNET related activities of Laboratory for Intelligent Systems and Dept. of Information and Knowledge Engineering http://lisp.vse.cz/~berka/MLNet.html

2. (c) Petr Berka, LISp, 20002 Research ä probabilistic methods - decomposable probability models and bayesian networks ä symbolic methods - generalized association rules and decision rules ä logical calculi for knowledge discovery in databases

3. (c) Petr Berka, LISp, 20003 People Jiří IvánekRadim Jiroušek Petr Berka Jan Rauch Tomáš KočkaVojtěch Svátek

4. (c) Petr Berka, LISp, 20004 Software LISp-Miner ä two data mining procedures: 4FT Miner (generalised association rules) and KEX (decision rules), ä large preprocessing module including SQL, ä output of rules in database format enables the users to implement own interpretation procedures.

5. (c) Petr Berka, LISp, 20005 LISP-Miner procedures ä 4FT-Miner (GUHA procedure) generalised association rules in the form Ant ~ Suc / Cond ä KEX weighted decision rules in the form Ant ==> C (weight)

6. (c) Petr Berka, LISp, 20006 4FT-Miner Data Matrix: CLIENTS LOANS Id Age Sex Salary District Amount Payment Months Quality 1 45 F 28 000 Prague 48 000 1 000 48 good........................... 70 000 18 M 12 000 Brno 36 000 2 000 18 bad Problem: Are there segments of clients SC and segments of loans SL such that To be in SC is at 90% equivalent to have a loan from SL and there is at least 100 such clients Ant is at 90% equivalent to Suc Ant  0.90%, 100 Suc is true iff a/(a+b+c)  0.9  a  100 Suc  Suc a - number of objects satisfying Ant and Suc Ant a b b- number of objects satisfying Ant and not satisfying Suc  Ant c d c- number of objects not satisfying Ant and satisfying Suc d- number of objects satisfying neither Ant nor Suc

7. (c) Petr Berka, LISp, 20007 4FT Miner Input: Data matrix, quantifier  0.90%, 100 Derived attributes for SC (possible Ant): Age (7 values), Sex (2 values), Salary (3 values), District (77 values) Derived attributes for SL (possible Suc): Amount (6 values), Duration (5 values), Quality (2 values) Output: All Ant  0.90%, 100 Suc true in data matrix (5 equivalences from about 5 milions possible relations) an example: Age(20 - 30)  Sex(F)  Salary(low)  District (Prague)  0.90%, 100 Amount<20,50)  Quality(Bad) Suc  Suc a/(a+b+c) = 0.95  0.9 Ant 950 30  950  100  Ant 20 69000

8. (c) Petr Berka, LISp, 20008 KEX - classification

9. (c) Petr Berka, LISp, 20009 KEX - learning

10. (c) Petr Berka, LISp, 200010 LISp-Miner

11. (c) Petr Berka, LISp, 200011 LISp-Miner

12. (c) Petr Berka, LISp, 200012 LISp-Miner

13. (c) Petr Berka, LISp, 200013 LISp-Miner

14. (c) Petr Berka, LISp, 200014 4FT Miner and KEX Applications ä truck reliability assessment ä quality control in a brewery ä segmentation of clients of a bank ä short-term electric load prediction

15. (c) Petr Berka, LISp, 200015 LISp Miner References:  Berka,P. - Ivanek,J.: Automated Knowledge Acquisition for PROSPECTOR-like Expert Systems. In: (Bergadano, deRaedt eds.) Proc. ECML'94, Springer 1994, 339-342.  Berka,P. - Rauch,J.: Data Mining using GUHA and KEX. In: (Callaos, Yang, Aguilar eds.) 4th. Int. Conf. on Information Systems, Analysis and Synthesis ISAS'98, 1998, Vol 2, 238- 244.  Rauch,J.: Classes of Four Fold Table Quantifiers. In: (Zytkow, Quafafou eds.) Principles of Data Mining and Knowledge Discovery. Springer 1998, 203 - 211.

16. (c) Petr Berka, LISp, 200016 Datasets PKDD‘99 Discovery Challenge data (http://lisp.vse.cz/pkdd99/chall.htm) ä financial data: clients of a bank, their accounts, transactions, loans etc, ä medical data: patients with collagen disease

17. (c) Petr Berka, LISp, 200017 Financial data

18. (c) Petr Berka, LISp, 200018 Medical data

19. (c) Petr Berka, LISp, 200019 Other activities ä Organized conferences ä Teaching (in czech) ä KDD ä KDD seminar ä ML http://lisp.vse.cz/ecml97/ http://lisp.vse.cz/pkdd99/

20. (c) Petr Berka, LISp, 200020 New projects SOL-EU-NET project „Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise“ (supported by EU grant IST-1999-11.495) (supported by EU grant IST-1999-11.495)