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|Title:||Predictive Sequence Miner in ILP Learning (Extended Abstract)|
Vítor Santos Costa
|Abstract:||In this work we present an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. The main idea behind XMuSer consists of exploiting frequent sequence mining, an eﬃcient and direct method to learn temporal patterns in the form of sequences. The eﬃciency of XMuSer comes from a new coding methodology and on the use of a predictive sequential miner, which ﬁnds discriminative frequent patterns. After ﬁnding the discriminative sequences, we map the most interesting ones into a new table that encodes the multi-relational temporal information. The original database is enlarged with a new table that encodes the temporal information in the form of sequences. The last step of our framework consists of applying an ILP algorithm to learn a theory on the enlarged relational database. We evaluate our framework by addressing three classiﬁcation multi-relational problems.|
|Appears in Collections:||LIAAD - Indexed Articles in Conferences|
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