Geospatial Data Mining
Objectives
Data Mining based on geo-referenced data has different characteristics from Data Mining carried out based on business data. Although there is a large number of coincidences between them, there are also some differences, which even though they are not very numerous, are very important and should not be neglected. This course aims to present the Data Mining methodology, as well as its main tools, and also emphasize the specificities that exist in the exploration of georeferenced data. In fact, the fundamental objective is to ensure that students develop their skills to analyze problems based on data, and can use that competence to extract the maximum value from the technologies that we have at our disposal today. Thus, at the end of the course, students must have a good understanding of the main Data Mining tools, as well as a critical spirit regarding their application in the context of geographic information science.
General characterization
Code
200024
Credits
7.5
Responsible teacher
Fernando José Ferreira Lucas Bação
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
No requirements.
Bibliography
Han, J., Kamber, M. 2006, Data Mining ¿ Concepts and Techniques, Morgan Kaufmann, Elsevier Inc.
Mitchell, T., (1997) Machine Learning, McGraw Hill.
Teaching method
The course is based on self-study, coaching sessions and conducting tutorials and exercises. In the tutorials, students have at their disposal a "script" that allows you to solve the proposed problem, in the case of the exercises is tested student autonomy.
The evaluation includes an examination (40%), a practical project (60%) given on how to report and a presentation.
Evaluation method
Individual Project 80%
Exam 20%
Subject matter
The syllabus is organized in 5 Learning Units (LU):
LU1. Introduction to Data Mining
LU2. Data Mining in the geographic information science context
LU3. The role of Data in Data Mining
LU4. Unsupervised Classification (clustering)
LU5. Supervised Classification (predictive modelling)
Programs
Programs where the course is taught: