In spatial domains, objects present high heterogeneity and are connected by several relationships to form complex networks. Mining spatial networks can provide information on both the objects and their interactions. In this work we propose a descriptive data mining approach to discover relational disjunctive patterns in spatial networks. Relational disjunctive patterns permit to represent spatial relationships that occur simultaneously with or alternatively to other relationships. Pruning of the search space is based on the anti-monotonicity property of support. The application to the problem of urban accessibility proves the viability of the proposal.
Relational learning of disjunctive patterns in spatial networks
LOGLISCI, CORRADO;CECI M;MALERBA, Donato
2010-01-01
Abstract
In spatial domains, objects present high heterogeneity and are connected by several relationships to form complex networks. Mining spatial networks can provide information on both the objects and their interactions. In this work we propose a descriptive data mining approach to discover relational disjunctive patterns in spatial networks. Relational disjunctive patterns permit to represent spatial relationships that occur simultaneously with or alternatively to other relationships. Pruning of the search space is based on the anti-monotonicity property of support. The application to the problem of urban accessibility proves the viability of the proposal.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
