A Pareto-dominant clustering approach for Pareto-frontiers

anaging large and confusing sets of increasing data is a well-known problem in Data Mining. Since compromises in many use cases like Recommender Systems or preference-based applications are becoming more and more usual, it is very useful to cluster sets of promising results in order to get an overview and present them properly. In this paper we present the Pareto-dominance as a very suitable and promising approach to cluster objects over better than relationships. In order to meet someones desires, one can tip the balance of the final results to the more favored dimension if no decision for allocating objects is possible.

Metadaten
Author:	Johannes Kastner GND, Markus Endres GND, Werner Kießling GND
URN:	urn:nbn:de:bvb:384-opus4-594223
Frontdoor URL	https://opus.bibliothek.uni-augsburg.de/opus4/59422
URL:	http://ceur-ws.org/Vol-1810/DOLAP_short_paper_13.pdf
URL:	https://nbn-resolving.org/urn:nbn:de:0074-1810-9
ISSN:	1613-0073OPAC
Parent Title (English):	Nineteenth International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2017), colocated with EDBT/ICDT 2017, Venice, Italy, March 21, 2017
Publisher:	CEURS-WS
Editor:	Yannis Ioannidis, Julia Stoyanovich, Giorgio Orsi
Type:	Part of a Book
Language:	English
Year of first Publication:	2017
Publishing Institution:	Universität Augsburg
Release Date:	2020/01/22
First Page:	1
Last Page:	5
Series:	CEUR Workshop Proceedings ; Vol-1810
Institutes:	Fakultät für Angewandte Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Datenbanken und Informationssysteme
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):	CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)

Open Access