RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators

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Abstract

We introduce two algorithms, RecurSIA and RRT, designed to increase the compression factor achievable using point-set cover algorithms based on the SIA and SIATEC pattern discovery algorithms. SIA computes the maximal translatable patterns (MTPs) in a point set, while SIATEC computes the translational equivalence class (TEC) of every MTP in a point set, where the TEC of an MTP is the set of translationally invariant occurrences of that MTP in the point set. In its output, SIATEC encodes each MTP TEC as a pair, ⟨P,V⟩, where P is the first occurrence of the MTP and V is the set of non-zero vectors that map P onto its other occurrences. RecurSIA recursively applies a TEC cover algorithm to the pattern P, in each TEC, ⟨P,V⟩, that it discovers. RRT attempts to remove translators from V in each TEC without reducing the total set of points covered by the TEC. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.

Original languageEnglish
Title of host publicationMachin Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019 Würzburg, Germany, September 16–20, 2019 Proceedings, Part II
EditorsPeggy Cellier, Kurt Driessens
Number of pages9
Volume1168
Place of PublicationCham, Switzerland
PublisherSpringer
Publication date2020
Pages485-493
ISBN (Print)978-3-030-43886-9
ISBN (Electronic)978-3-030-43887-6
DOIs
Publication statusPublished - 2020
EventInternational Workshop on Machine Learning and Music - Würzburg, Germany
Duration: 16 Sep 201916 Sep 2019
Conference number: 12
https://musml2019.weebly.com/

Conference

ConferenceInternational Workshop on Machine Learning and Music
Number12
Country/TerritoryGermany
CityWürzburg
Period16/09/201916/09/2019
Internet address
SeriesCommunications in Computer and Information Science
Volume1168
ISSN1865-0929

Keywords

  • COSIATEC
  • Data compression
  • Forth’s algorithm
  • Geometric pattern discovery in music
  • Music analysis
  • Pattern discovery
  • Point sets
  • SIATEC
  • SIATECCompress

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