Sequential Pattern Mining in Multi-relational Datasets

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2010

Autores

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

Ata de Conferência Internacional

Título: CURRENT TOPICS IN ARTIFICIAL INTELLIGENCE

Páginas: 121-130

13th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA 2009)

Seville, SPAIN, NOV 09-13, 2009

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-003-AJ3

DOI: 10.1007/978-3-642-14264-2_13

Abstract (EN): We present a framework designed to mine sequential temporal patterns from multi-relational databases. In order to exploit logic-relational information without using aggregation methodologies, we convert the multi-relational dataset into what we name a multi-sequence database. Each example in a multi-relational target table is coded into a sequence that combines intra-table and inter-table relational temporal information. This allows us to find heterogeneous temporal patterns through standard sequence miners. Our framework is grounded in the excellent results achieved by previous propositionalization strategies. We follow a pipelined approach, where we first use a sequence miner to find frequent sequences in the multi-sequence database. Next, we select the most interesting findings to augment the representational space of the examples. The most interesting sequence patterns are discriminative and class correlated. In the final step we build a classifier model by taking an enlarged target table as input to a classifier algorithm. We evaluate the performance of this work through a motivating application, the hepatitis multi-relational dataset. We prove the effectiveness of our methodology by addressing two problems of the hepatitis dataset.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10