ARTICLE lapidot-rr-02-48b/IDIAP Dichotomy Between Clustering Performance and Minimum Distortion in Piecewise-Dependent-Data (PDD) Clustering Lapidot, I. Guterman, H. EXTERNAL http://publications.idiap.ch/attachments/reports/2002/rr02-48.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/lapidot-rr02-48 Related documents to be published in IEEE Signal Processing Letters 2003 IDIAP-RR 02-48 In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to minimize a given distance function. In this paper 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 distortion,',','), i.e. meaningful clustering. REPORT lapidot-rr02-48/IDIAP Dichotomy Between Clustering Performance and Minimum Distortion in Piecewise-Dependent-Data (PDD) Clustering Lapidot, I. Guterman, H. EXTERNAL http://publications.idiap.ch/attachments/reports/2002/rr02-48.pdf PUBLIC Idiap-RR-48-2002 2002 IDIAP and Ben-Gurion University of the Negev, Israel Martigny, Switzerland accepted for publication in IEEE Signal Processing Letters In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-Dependent- Data (PDD). In clustering it is frequently needed to minimize a given distance function. In this paper 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 distortion,',','), i.e. meaningful clustering.