Sequential pattern knowledge in multi-relational learning

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Ferreira, CA (Author) Other; Gama, J (Author) FEP; Costa, VS (Author) FCUP

Conference proceedings International

Title: Computer and Information Sciences II - 26th International Symposium on Computer and Information Sciences, ISCIS 2011

Pages: 539-545

26th Annual International Symposium on Computer and Information Science, ISCIS 2011

London, 26 September 2011 through 28 September 2011

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Other information

Authenticus ID: P-008-GZC

DOI: 10.1007/978-1-4471-2155-8-69

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 sequence 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. © 2012 Springer-Verlag London Limited.

Language: English

Type (Professor's evaluation): Scientific