Publication in the Diário da República: Despacho 23/08/2011
6 ECTS; 1º Ano, 2º Semestre, 24,0 T + 8,0 TP , Cód. 300012.
Lecturer
(1) Docente Responsável
(2) Docente que lecciona
Prerequisites
Objectives
The main objectives of this course are:
1 - Transmit the concepts, benefits and constraints of implementation and use of databases and prospecting tools of data;
2 - To know the methods, techniques and DM algorithms used in medicine;
3 - Implement specific applications in healthcare using computational tools for data mining.
Skills to be acquired:
Recognize the need and added value of data mining applications to build knowledge in the health sector;
Understand and be able to overcome the difficulties of implementing data mining applications;
Mastering the techniques, methods and data mining algorithms used in preprocessing and processing of health data;
Ability to deploy applications for prospecting data.
Program
Data Warehouses;
Data Mining (DM)
Introduction to data mining in medicine
Preprocessing data techniques
Classification data
Decision trees
Association rules
Agglomeration
Open source software: Weka, R
Case studies
Evaluation Methodology
The evaluation method consists of making a written test, with a weight of 40% of the final classification, and the design and implementation of a tool for prospecting data, with a weight of 60% of the final classification. To obtain approval for the course the student must achieve a final grade, averaged from the two components of assessment, equal to or greater than 9.5.
Bibliography
- Chen, H. e Fuller, S. e Friedman, C. e Hersh, W. (2005). Medical Informatics: Knowledge Management and Data Mining in Biomedicine. (Vol. 8). (pp. 1--). USA: Springer
- Witten, I. e Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. (Vol. 1). (pp. 1--). Germany: Morgan Kaufmann
Teaching Method
Considering the objectives of the Data Mining Techniques course teaching method is to practice oral presentation of the syllabus provided (lectures), the presentation and discussion of topics related to them (theoretical-practical classes) and also in handling data mining tools (laboratory classes). Are also provided tutorial orientation sessions, and used the IPT e-learning platform as a tool for disseminating information, answering questions, posting handouts, exercise sheets etc..
Software used in class
SAS - Business Analytics and Business Intelligence Software
