Improving Clustering Performance by Incorporating Uncertainty

Maha Bakoben, Tony Bellotti, Niall Adams

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)


In more challenging problems the input to a clustering problem is not raw data objects, but rather parametric statistical summaries of the data objects. For example, time series of different lengths may be clustered on the basis of estimated parameters from autoregression models. Such summary procedures usually provide estimates of uncertainty for parameters, and ignoring this source of uncertainty affects the recovery of the true clusters. This paper is concerned with the incorporation of this source of uncertainty in the clustering procedure. A new dissimilarity measure is developed based on geometric overlap of confidence ellipsoids implied by the uncertainty estimates. In extensive simulation studies and a synthetic time series benchmark dataset, this new measure is shown to yield improved performance over standard approaches.
Original languageEnglish
Pages (from-to)28-34
Number of pages7
JournalPattern Recognition Letters
Early online date11 Mar 2016
Publication statusPublished - 1 Jul 2016


  • Clustering with uncertainty
  • Ellipsoid dissimilarity measures
  • Confidence ellipsoids
  • Time series clustering


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