University of Economics, Prague

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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

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Research

• probabilistic methods - decomposable probability
  models and bayesian networks
• symbolic methods - generalized association rules
  and decision rules
• logical calculi for knowledge discovery in
  databases

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People
Petr Berka
Jirí Ivánek
Radim Jiroušek
Jan Rauch
Tomáš Kocka
Vojtech Svátek
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Software

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

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LISP-Miner procedures

• 4FT-Miner (GUHA procedure)
• generalised association rules in the form
• Ant Suc / Cond
• KEX
• weighted decision rules in the form
• Ant gt C (weight)

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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/(abc) ? 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
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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 Amountlt20,50) ?
  Quality(Bad)
• Suc
  ?Suc
• a/(abc) 0.95 ? 0.9
  Ant 950 30
• ? 950 ? 100 ?Ant
  20 69000

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KEX - classification
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KEX - learning
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LISp-Miner
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LISp-Miner
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LISp-Miner
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LISp-Miner
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4FT Miner and KEX

• Applications
• truck reliability assessment
• quality control in a brewery
• segmentation of clients of a bank
• short-term electric load prediction

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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.

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Datasets

• PKDD99 Discovery Challenge data
  (http//lisp.vse.cz/pkdd99/chall.htm)
• financial data clients of a bank, their
  accounts, transactions, loans etc,
• medical data patients with collagen disease

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Financial data
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Medical data
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Other activities

• Organized conferences

• Teaching (in czech)
• KDD
• KDD seminar
• ML

http//lisp.vse.cz/ecml97/
http//lisp.vse.cz/pkdd99/
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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)