Dichotomy between clustering performance and minimum distortion in piecewise-dependent-data (PDD) clustering

Itshak Lapidot, Hugo Guterman

Research output: Contribution to journalArticlepeer-review

Abstract

In many time-series such as speech, biosignals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration, such data can be referred to as piecewise-dependent data (PDD). In clustering, it is frequently needed to minimize a given distance function. In this letter, we will show that in PDD clustering there is a contradiction between the desire for high resolution (short segments and low distance) and high accuracy (long segments and high distance), i.e., meaningful clustering.

Original languageEnglish
Pages (from-to)98-100
Number of pages3
JournalIEEE Signal Processing Letters
Volume10
Issue number4
DOIs
StatePublished - 1 Apr 2003

Keywords

  • Clustering
  • Minimal distance
  • Piecewise-dependent data
  • Self-organizing maps

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