Abstract
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.
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Acknowledgments
This work was supported by the Portuguese Foundation for Science and Technology (FCT) under the projects KDUS (PTDC/EIA-EIA/098355/2008) and HORUS (PTDC/EIA-EIA/100897/2008). Carlos Abreu Ferreira was financially supported by the Portuguese Polytechnic Institute of Porto (ISEP/IPP).
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Ferreira, C.A., Gama, J., Costa, V.S. (2011). Sequential Pattern Knowledge in Multi-Relational Learning. In: Gelenbe, E., Lent, R., Sakellari, G. (eds) Computer and Information Sciences II. Springer, London. https://doi.org/10.1007/978-1-4471-2155-8_69
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DOI: https://doi.org/10.1007/978-1-4471-2155-8_69
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