Large-Scale Similarity-Based Time Series Mining

  • Diego F. Silva
  • Gustavo E. A. P. A. Batista
  • Eamonn Keogh


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.

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SILVA, Diego F.; BATISTA, Gustavo E. A. P. A.; KEOGH, Eamonn. Large-Scale Similarity-Based Time Series Mining. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 31. , 2018, Natal. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . ISSN 2763-8820. DOI: