DSpace DSpace UPC
 Català   Castellano   English  

E-prints UPC >
Altres >
Enviament des de DRAC >

Empreu aquest identificador per citar o enllaçar aquest ítem: http://hdl.handle.net/2117/16934

Arxiu Descripció MidaFormat
qimiePostProc.pdf247,47 kBAdobe PDFThumbnail

Citació: Balcazar, J. Two measures of objective novelty in association rule mining. A: Pacific-Asia Conference on Knowledge Discovery and Data Mining. "New frontiers in applied data mining: PAKDD 2009 International Workshops: Bangkok, Thailand, April 27-30, 2009: revised selected papers". Bangkok: Springer, 2009, p. 76-98.
Títol: Two measures of objective novelty in association rule mining
Autor: Balcázar Navarro, José Luis Veure Producció científica UPC
Editorial: Springer
Data: 2009
Tipus de document: Conference report
Resum: Association rule mining is well-known to depend heavily on a support threshold parameter, and on one or more thresholds for intensity of implication; among these measures, confidence is most often used and, sometimes, related alternatives such as lift, leverage, improvement, or all-confidence are employed, either separately or jointly with confidence. We remain within the support-and-confidence framework in an attempt at studying complementary notions, which have the goal of measuring relative forms of objective novelty or surprisingness of each individual rule with respect to other rules that hold in the same dataset. We measure novelty through the extent to which the confidence value is robust, taken relative to the confidences of related (for instance, logically stronger) rules, as opposed to the absolute consideration of the single rule at hand. We consider two variants of this idea and analyze their logical and algorithmic properties. Since this approach has the drawback of requiring further parameters, we also propose a framework in which the user sets a single parameter, of quite clear intuitive semantics, from which the corresponding thresholds for confidence and novelty are computed.
ISBN: 9783642146398
URI: http://hdl.handle.net/2117/16934
DOI: 10.1007/978-3-642-14640-4_6
Apareix a les col·leccions:Altres. Enviament des de DRAC
LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge. Ponències/Comunicacions de congressos
Departament de Ciències de la Computació. Ponències/Comunicacions de congressos

Stats Mostra les estadístiques d'aquest ítem

SFX Query

Aquest ítem (excepte textos i imatges no creats per l'autor) està subjecte a una llicència de Creative Commons Llicència Creative Commons
Creative Commons


Valid XHTML 1.0! Programari DSpace Copyright © 2002-2004 MIT and Hewlett-Packard Comentaris
Universitat Politècnica de Catalunya. Servei de Biblioteques, Publicacions i Arxius