An incremental facility location clustering with a new hybrid constrained pseudometric

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10425607" target="_blank" >RIV/00216208:11310/23:10425607 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/23:10425607 RIV/49777513:23520/23:43969884

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8Ufb9qYPDz" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8Ufb9qYPDz</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patcog.2023.109520" target="_blank" >10.1016/j.patcog.2023.109520</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An incremental facility location clustering with a new hybrid constrained pseudometric

Popis výsledku v původním jazyce

The Euclidean metric, one of the classical similarity measures applied in clustering algorithms, has drawbacks when applied to spatial clustering. The resulting clusters are spherical and similarly sized, and the edges of objects are considerably smoothed. This paper proposes a novel hybrid constrained pseudometric formed by the linear combination of the Euclidean metric and a pseudometric plus penalty. The pseudometric is used in a new deterministic incremental heuristic facility location algorithm (IHFL). Our method generates larger, isotropic, and partially overlapping clusters of different sizes and spatial densities, better adapting to the surface complexity than the classical non-deterministic clustering. Cluster properties are used to derive new features for supervised/unsupervised learning. Possible applications are the classification of point clouds, their simplification, detection, filtering, and extraction of different structural patterns or sampled objects. Experiments were run on point clouds derived from laser scanning and images.

Název v anglickém jazyce

An incremental facility location clustering with a new hybrid constrained pseudometric

Popis výsledku anglicky

The Euclidean metric, one of the classical similarity measures applied in clustering algorithms, has drawbacks when applied to spatial clustering. The resulting clusters are spherical and similarly sized, and the edges of objects are considerably smoothed. This paper proposes a novel hybrid constrained pseudometric formed by the linear combination of the Euclidean metric and a pseudometric plus penalty. The pseudometric is used in a new deterministic incremental heuristic facility location algorithm (IHFL). Our method generates larger, isotropic, and partially overlapping clusters of different sizes and spatial densities, better adapting to the surface complexity than the classical non-deterministic clustering. Cluster properties are used to derive new features for supervised/unsupervised learning. Possible applications are the classification of point clouds, their simplification, detection, filtering, and extraction of different structural patterns or sampled objects. Experiments were run on point clouds derived from laser scanning and images.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Pattern Recognition

ISSN

0031-3203

e-ISSN

1873-5142

Svazek periodika

141

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

109520

Kód UT WoS článku

000992216400001

EID výsledku v databázi Scopus

2-s2.0-85153671852