| dc.creator | Gómez, Sergio Alejandro | |
| dc.creator | Chesñevar, Carlos Iván | |
| dc.date | 2003-10 | |
| dc.date | 2003-10 | |
| dc.date | 2012-10-22T12:26:31Z | |
| dc.identifier | http://sedici.unlp.edu.ar/handle/10915/22714 | |
| dc.description | Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is checked. In such a setting clusters can overlap, and a new instance (or example) can be assigned to more than one cluster. In many cases, determining to which cluster this new instance actually belongs requires a qualitative analysis rather than a numerical one.
In this paper we present a novel approach to solve this problem by combining defeasible argumentation and a clustering algorithm based on the Fuzzy Adaptive Resonance Theory neural network model. The proposed approach takes as input a clustering algorithm and a background theory. Given a previously unseen instance enew, it will be classified using the clustering algorithm. If a conflicting situation arises, argumentation will be used in order to consider the user’s preference criteria for classifying examples. | |
| dc.description | Eje: Agentes y Sistemas Inteligentes (ASI) | |
| dc.description | Red de Universidades con Carreras en Informática (RedUNCI) | |
| dc.format | application/pdf | |
| dc.format | 601-612 | |
| dc.language | en | |
| dc.relation | IX Congreso Argentino de Ciencias de la Computación | |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) | |
| dc.subject | Ciencias Informáticas | |
| dc.title | Combining argumentation and clustering techniques in pattern classification problems | |
| dc.type | Objeto de conferencia | |
| dc.type | Objeto de conferencia | |
