• Anna Monreale
  Università di Pisa, Knowledge Discovery and Data Mining Lab
  annam [at] di [dot] unipi [dot] it

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.

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

• Slides (see Calendar).

• Gordon S. Linoff e Michael J. Berry. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley, 2011.

• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
  • http://www-users.cs.umn.edu/~kumar/dmbook/index.php

• 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

0. Iris. (for details see https://archive.ics.uci.edu/ml/datasets/iris)

1. Human Resources. (for details see https://www.kaggle.com/ludobenistant/hr-analytics)

2. Telco Churn. (for details see http://didawiki.di.unipi.it/doku.php/dm/mains.santanna.dm4crm.2016)

3. Adult. (for details see https://archive.ics.uci.edu/ml/datasets/Adult)

4. Titanic. (for details see https://www.kaggle.com/c/titanic)