Nowadays during clinical studies, both retrospective studies and clinical trials, a huge amount of data is collected leading to the construction of big datasets. Data mining is the process of evaluating existing datasets to extract new insights from them. Using Data mining techniques researchers are able to extract import information from data. These methods belong to Machine Learning (ML), statistics, and databse systems areas. The course presents a set of ML techniques (clustering and association rules) that can be applied to the dataset to analyse the data. The results are the discover of new patterns and rules that can be applied to construct new knowledge or to develop new decision support systems.

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The course is divided in lessons (12 hours) and laboratory work (8 hours). 1. Introduction 2. Data storage and Data Cleaning 3. Missing Data 4. Clustering 5. Associative Rules 6. Applications During the laboratory lessons the students will apply data mining techniques to real data.

A distanza in modalità sincrona

Sviluppo di project work in team

P.D.1-1 - Gennaio