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.
| 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 |
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)