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Data Mining

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

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  1. Data Mining dr Iwona Schab 27-18 września 2012

  2. 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.

  3. 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.

  4. 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

  5. Statistical Analysis?

  6. 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

  7. KDD Process(Knowledge Discovery in Database)

  8. Software www.sgh.waw.pl/ogolnouczelniane/ci/aplikacje/oprogramowanie/ • SAS/STAT • SAS Enterprise Miner --- • Other: Statistica, SPSS • WEKA

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