Please use this identifier to cite or link to this item:
Title: Predictive Sequence Miner in ILP Learning (Extended Abstract)
Authors: João Gama
Carlos Ferreira
Vítor Santos Costa
Issue Date: 2011
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.
metadata.dc.type: conferenceObject
Appears in Collections:LIAAD - Indexed Articles in Conferences

Files in This Item:
File Description SizeFormat 
PS-07737.pdf185.82 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.