Exploring multi-relational temporal databases with a propositional sequence miner

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Ferreira, CA (Autor) Outra; João Gama (Autor) FEP; Costa, VS (Autor) FCUP

Revista

Título: Progress in Artificial Intelligence

Vol. 4 Nº 1-2

Páginas: 11-20

ISSN: 2192-6352

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00K-A5T

DOI: 10.1007/s13748-015-0065-x

Abstract (EN): In this work, we introduce the MuSer, a propositional framework that explores temporal information available in multi-relational databases. At the core of this system is an encoding technique that translates the temporal information into a propositional sequence of events. By using this technique, we are able to explore the temporal information using a propositional sequence miner. With this framework, we mine each class partition individually and we do not use classical aggregation strategies, like window aggregation. Moreover, in this system we combine feature selection and propositionalization techniques to cast a multi-relational classification problem into a propositional one. We empirically evaluate the MuSer framework using two real databases. The results show that mining each partition individually is a time- and memory-efficient strategy that generates a high number of highly discriminative patterns.

Idioma: Inglês

Tipo (Avaliação Docente): Científica