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Constrained Sequential Pattern Knowledge in Multi-relational Learning

Title
Constrained Sequential Pattern Knowledge in Multi-relational Learning
Type
Article in International Conference Proceedings Book
Year
2011
Authors
Carlos Abreu Ferreira
(Author)
Other
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Joao Gama
(Author)
FEP
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Vitor Santos Costa
(Author)
FCUP
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Conference proceedings International
Pages: 282-296
15th Portuguese Conference on Artificial Intelligence (EPIA 2011)
Lisbon, PORTUGAL, OCT 10-13, 2011
Scientific classification
FOS: Natural sciences > Computer and information sciences
Other information
Authenticus ID: P-002-VYE
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.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 15
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