Abstract:

Students are exposed to many cutting-edge information technologies every day while they use their computers or smartphones, although most of this applied research is used in a transparent manner for them. These technologies can be applied as new methodologies for trainee researchers in different engineering fields, reducing the gap between students and research.

One of the most important technologies developed in recent years is data mining. Data mining is an interdisciplinary subfield of computer science, whose main aim is to discover patterns in large sets of data. Data mining is present many day-to-day activities, such as in most of the recommendation or shopping online platforms. For instance, data mining is applied to find music bands related to the music taste of a user or to provide other items that may be of interest after a purchase process.

Data mining techniques involves several approaches: association rules, clustering, classification and summarization, among others. In particular, association rule learning searches for interesting relationships between variables in datasets. This tecnique has clear potential in Applied Engineering and it was used for the present work.

In this work, the association rule learning technique was used to find relations between the characteristics of a certain type of coastal features (shoreline undulations) and typical Coastal Engineering variables as wave height or tidal range. A student without experience was the responsible of the work, using this methodology as introduction into research. During the conference, the results obtained after the application of the data mining techniques, as well as the experience of the student will be presented, highlighting the advantages of this type of technologies in both fields.

Keywords:

Data mining, Database, Coastal features, Graduate students.