@inproceedings{6b952741dbe24a798710fda4987dd77f,
title = "Categorizing Volcanic Seismic Events with Unsupervised Learning",
abstract = "We explored three different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. The BFR classifier with k=2 was chosen as the best out of 27 explored models statistically (p<0.05), reaching a mean of accuracy score of 88\%. This result represents a satisfactory and competitive classification performance when compared to the state of art methods. The CURE classifier with k=3 attained a mean of accuracy value of 87\% at p<0.05, allowing it to be the only model capable of detecting seismic events with overlapping signals. Therefore, the proposed clustering-based exploration was effective in providing competitive models for seismic events classification and overlapped signal detection.",
keywords = "BFR, CURE, clustering methods, k-means, unsupervised learning, volcanic seismic event categorization",
author = "Adrian Duque and Kevin Gonzalez and Noel Perez and Benitez, \{Diego S.\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 ; Conference date: 07-08-2020 Through 09-08-2020",
year = "2020",
month = aug,
day = "7",
doi = "10.1109/ColCACI50549.2020.9247854",
language = "Ingl{\'e}s",
series = "2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Orjuela-Canon, \{Alvaro David\}",
booktitle = "2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings",
}