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Teaching GuideTerm Faculty of Humanities |
| Grao en Xestión Dixital de Información e Documentación |
 Subjects |
| Data Mining |
| Contents |
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| Identifying Data | 2022/23 | |||||||||||||
| Subject | Data Mining | Code | 710G04030 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Third | Optional | 6 | ||||||||||
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| Topic | Sub-topic |
| Introduction to data mining. |
Preliminary concepts. Types of data mining problems: description, classification, prediction, clustering, anomaly detection, etc. Types of learning: supervised and unsupervised. |
| Unsupervised classification or clustering methods. |
Basic concepts. Hierarchical classification methods. Partitioning clustering methods. Case studies in information science and documentation. |
| Supervised classification methods. |
Basic concepts. Main models of supervised classification or pattern recognition. Validation of classification models (how well do they predict?). Case studies in information science and documentation. |
| Advanced regression methods. |
Introduction. Univariate and multivariate regression models. Selection of relevant variables. Validation of regression models (how well does it fit the data, how well does it make predictions). Case studies in information science and documentation. |
| Time series |
Basic concepts. Descriptive time series analysis. Practical use of time series models. Case studies. |
| Statistical techniques for text mining and information retrieval. | Basic concepts. Practical cases of application of text mining. |
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