Constrained Sequential Pattern Knowledge in Multi-relational Learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Carlos Abreu Ferreira (Autor) Outra; Joao Gama (Autor) FEP; Vitor Santos Costa (Autor) FCUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE

Páginas: 282-296

15th Portuguese Conference on Artificial Intelligence (EPIA 2011)

Lisbon, PORTUGAL, OCT 10-13, 2011

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-002-VYE

DOI: 10.1007/978-3-642-24769-9_21

Abstract (EN): In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15