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Project STULONG Some analytical work at EuroMISE and University of Economics, Prague

Project STULONG Some analytical work at EuroMISE and University of Economics, Prague. Jan Rauch EuroMISE – Cardio, University of Economics, Prague This work is supported by the project LN00B107 of the Ministry of Education of the Czech Republic. Content.

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Project STULONG Some analytical work at EuroMISE and University of Economics, Prague

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  1. Project STULONG Some analytical work at EuroMISE and University of Economics, Prague Jan Rauch EuroMISE – Cardio, University of Economics, Prague This work is supported by the project LN00B107 of the Ministry of Education of the Czech Republic

  2. Content • LISp-Miner system – an overview • Procedure 4ft-Miner and Death Causes • Procedure KL-Miner and Ordinal depenedencies in Entry • Procedure SDS-Miner and Death Causes • LISp-Miner system – some more info

  3. LISp – Miner • Academic software system for KDD research and teaching developed at University of Economics, Prague • Analytical procedures based on GUHA principle • Machine learning procedure KEX • Data transformation procedures • Several related research topic • See http://lispminer.vse.cz(new version of home page forthcoming)

  4. Analytical procedures based on GUHA principle Simple definition of large set of relevant questions Data Generation and verification of relevant questions All prime relevant assertions CF-Miner SDS-Miner 4ft-Miner KL-Miner SDKL-Miner SDCF-Miner (considerations)

  5. Procedure 4ft-Miner • Mines for generalised association rules including conditional rules and rules corresponding to statistical hypotheses tests • Implementation not based on A-priori • See http://lispminer.vse.cz/overview/4ftminer.html • More information see attached file ICDM02_TFDM_publ.pdf • STULONG - Death 4ft-Miner application example see http://lisp.vse.cz/challenge/ecmlpkdd2003/ and

  6. Procedure KL-Miner • Mines for Patterns Based on Contingency Tables • Implementation based on the same principles as 4ft-Miner • STULONG – Entry KL-Miner application example see attached file 1KL_Minr.pdf and • Published at ICDM2003 WORKSHOP - Foundations and  New Directions  of Data Mining, see http://www.cs.sjsu.edu/faculty/tylin/icdm03_workshop.html

  7. Procedure SDS -Miner • Mines for interesting couples of sets • Implementation based on the same principles as 4ft-Miner • STULONG - Death SDS-Miner application example see • Published at ACM Symposium on Applied Computing SAC 2004, see http://dmtrack.di.unito.it/

  8. LISp-Miner - further analytical procedures CF-Miner – described under name Pareto-Miner in the attached file ICDM02_TFDM_publ.pdf SDKL-Miner – in development, informally combination of SDS-Miner and KL-Miner SDCF-Miner – in development, informally combination of SDS-Miner and CF-Miner

  9. LISp – Miner - Machine learning procedure KEX seehttp://lispminer.vse.cz/overview/kex.html

  10. LISp – Miner Data transformation procedures DataSource – various input data transformation and explorations See http://lispminer.vse.cz/overview/DataSource.html TimeTransf – computation of characteristics of time-series See http://euromise.vse.cz/challenge2004/transform/index.html

  11. LISp – Miner – some related research topic • Logical calculi for KDD – see e.g. attached file PAKDD02_TFDM_publ.pdf • Automatic transformations of association rules into natural language see http://euromise.vse.cz/stulong-en/nl/index.php?page=nl An example: http://euromise.vse.cz/stulong-en/a-otazky/vv/detail.php?groupID=024&var=01&hypID=2751

  12. Automatic transformations of association rules into natural language - an example

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