Organizations and business are overwhelmed by the flood of data continuously collected into their data warehouses and arriving from external sources – the Web above all. Traditional exploratory techniques may fail to make sense of the data, due to its inherent complexity and size. 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, notably in the CRM domain.
Date | Topic | Learning material | |
---|---|---|---|
1. | 22.11.2011 - 11:00-13:00 and 16:00-18:00 | Introduction to Data Mining and the Knowledge Discovery Process | slides - Textbook: chapt. 1 |
2. | 23.11.2011 - 09:00-11:00 | Data understanding. Introduction to Weka | slides - Textbook: chapt. 2 (2.1, 2.2) and chapt. 3 (3.1, 3.2, 3.3) |
3. | 28.11.2011 - 11:00-13:00 and 14:00-16:00 | Clustering Analysis | slides - Textbook: chapt. 8 (8.1, 8.2, 8.5) |
4. | 29.11.2011 - 11:00-13:00 and 16:00-18:00 | Classification and predictive analysis | slides - Textbook: chapt. 4 (4.1, 4.2, 4.3, 4.4, 4.5) |
5. | 30.11.2011 - 16:00-18:00 | Pattern discovery and associaltion rule mining | slides - Textbook: chapt. 6 (6.1, 6.2) |
6. | 05.12.2011 - 09:00-13:00 | CRM applications. Big data and social network analysis. Data mining and privacy |
For MAINS master students (one-year degree) the exam of the Data Mining module consists in the evaluation of the two reports of exercises 1 and 2 above. For students of the two-year LM-MAINS degree the exam consists in the evaluation of the two reports of exercises 1 and 2 above, and an individual oral exam devoted to the discussion of aspects emerging from the exercises. The evaluation of the reports is the same for all components of the group (max 3 students oer group). The date of the first oral exam session of the LM-MAINS students will set by appointment, within January 2012.