Large-Scale Similarity-Based Time Series Mining
Measuring the (dis)similarity between time series is the main procedure of several algorithms for mining this kind of data, which is ubiquitous in the day-by-day of human beings. While providing satisfactory results, similaritybased methods usually suffer from a high time complexity. This work summarizes a thesis on developing algorithms that allow the similarity-based mining of temporal data in a large scale. The contributions of the thesis have implications in several data mining tasks, such as classification, clustering and motif discovery, as well as applications in music data science.