Large-Scale Similarity-Based Time Series Mining

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

Resumo


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.

Publicado
26/07/2018
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: https://doi.org/10.5753/ctd.2018.3656.