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Data Mining. dr Iwona Schab. Semester timetable. ORGANIZATIONAL ISSUES, INDTRODUCTION TO DATA MINING 1 Sources of data in business, administration, science and technology. 2 The process of discovering knowledge in data; the role of data mining in this process.

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data mining

Data Mining

dr Iwona Schab

27-18 września 2012

semester timetable
Semestertimetable
  • ORGANIZATIONAL ISSUES,
  • INDTRODUCTION TO DATA MINING
  • 1 Sources of data in business, administration, science and technology.
  • 2 The process of discovering knowledge in data; the role of data mining in this process.
  • 3 Data mining and Business Intelligence.
  • 4 SEMMA methodology.
  • 5 Data preparation: sampling, cleaning, normalization and standardization.
  • 6 Associationrulesdiscovery.
  • 7 Classification problems: case studies.
semester timetable1
Semestertimetable
  • 8 Rule induction systems: algorithms, knowledge representation.
  • 9 Decision trees: partition rules and pruning.
  • 10 Classification based on probability distributions: naive Bayes estimation and Bayesian networks.
  • 11 Grouping problems - case studies.
  • 12 Cluster analysis: combinatorial and hierarchical methods.
  • 13 Modeling response to direct mail marketing.
  • 14 Churnanalysis.
  • 15 Textmining.
  • 16 Web mining.
  • 17 Data mining in Life Science.
  • 18 Comparative analysis of algorithms implemented in SAS Enterprise Miner and WEKA software.
literature
Literature

Basic

  • Paolo Giudici, Applied Data Mining. Statistical Methods for Business and Industry, Wiley, New York 2011

Supplementary

  • Selectedpapers to be circulated
  • Daniel T.Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, New York 2005
  • Daniel T.Larose, DataMining Methods and Models, Wiley, New York 2006
data mining1
Data Mining
  • to mine = to extract (e.g. precious, hiddenresources from the Earth)
  • Differentdefinition and understandingdepending on user
  • New dysciplinedeveloped from computing and statistics
  • In-depthsearch to findadditionalinformation (previouslyunnoticed in the mass of data available)
  • Data preparation and „structuringunstructured” needed
      • Machine learning = finding relations and regularities in data
      • Generalisation from the observed data to newunobservedcase
software
Software

www.sgh.waw.pl/ogolnouczelniane/ci/aplikacje/oprogramowanie/

  • SAS/STAT
  • SAS Enterprise Miner

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  • Other: Statistica, SPSS
  • WEKA