A new dissimilarity measure in time-dependent experiments

Jongwoo Song

Research output: Contribution to journalArticlepeer-review

Abstract

Most distance measures used in unsupervised learning methods including the Euclidean distance and correlation-based distances disregard the time order of observations. In this paper, we consider a new dissimilarity measure that incorporates the time order of observations for time-dependent experiments. It measures the distance between a linear combination of two consecutive observations. To consider the length of time interval between observations, we use the same measure with the weight of time length, Δ ti. We show that this measure has larger asymptotic discriminating power than the Euclidean distance, and it also gives a good small sample performance.

Original languageEnglish
Pages (from-to)145-153
Number of pages9
JournalJournal of the Korean Statistical Society
Volume37
Issue number2
DOIs
StatePublished - Jun 2008

Keywords

  • 62H31
  • 68T05
  • Cluster analysis
  • Dissimilarity measure
  • primary
  • secondary
  • Time-dependent microarrays

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