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dm:dm-sam

Data Mining 2018

News

Goals

Data mining and knowledge discovery techniques emerged as an alternative approach, aimed at revealing patterns, rules and models hidden in the data, and at supporting the analytical user to develop descriptive and predictive models for a number of business problems. This short course focusses on the main applications scenarios of data mining to challenging problems in the broad CRM domain - Customer Relationship Management.

Syllabus

  • Clustering models. Discussion of real cases.
  • Patterns and association rule mining for market basket analysis.
  • Prediction models Discussion of real cases.

Textbooks

  • Slides (see Calendar).
  • Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. GUIDE TO INTELLIGENT DATA ANALYSIS. Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7

Reading about the "data analyst" job

  • Data, data everywhere. The Economist, Feb. 2010 download
  • Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 link
  • Welcome to the yotta world. The Economist, Sept. 2011 download

Calendar

Date Topic Learning material
01. 18.09.2018 Introduction to data mining and big data analytics. Data Understanding & Preparation 1-introduction-sa.pdf 2-dataunderstanding-sa.pdf 3-data_preparation-sa.pdf
02. 19.09.2018 knime: Data Understanding & Preparation. Clustering 4-clusteringintroduction-sa.pdf 5-kmeans-sa.pdf 6-dbscan-sa.pdf 01_titanic_data_understanding
03. 20.09.2018 Knime: Clustering. Classificazione. knime_clustering 7-classification-sa.pdf
04. 21.09.2018 Knime: Classificazione. Case Studies knime_classification calcio_infortuni.pdfmusicpref.pdf mensa.pdf

Datasets

dm/dm-sam.txt · Ultima modifica: 12/10/2018 alle 18:03 (9 mesi fa) da Anna Monreale