Data Mining and Processing

Description

The course will cover the following main topics, depending on the course hours allowed, with particular emphasis on
exploration of models:
1. The data mining process: why do we use data mining, what are the stages of data mining?
2. Data pre-processing: introduction to outlier detection and feature selection, feature extraction, etc.
3. Cluster analysis: discovery of algorithms for clustering crisp to uncertain data (optional)
4. Pattern mining and associative rules
1. Frequent pattern mining
2. Closed pattern mining
3. Sequential Pattern Search
4. Closed sequential pattern search
5. Graph mining
5. Classification and prediction (optional)

Compétences visées

After the course, the student should be able to:
1. Analyse data mining problems and reason about the most appropriate methods to apply to a given dataset
data set and knowledge extraction need.
2. Implement basic pre-processing, pattern mining, classification and clustering algorithms.
3. Apply and reflect on advanced algorithms for pre-processing, pattern mining, classification and clustering.
4. Work effectively in groups and evaluate algorithms on real-world problems.
5. Select, exploit and synthesise scientific and/or bibliographical information "

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