Predictive sequence miner in ILP learning

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

Authors

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

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 130-144

21st International Conference on InductiveLogic Programming, ILP 2011

Windsor Great Park, 31 July 2011 through 3 August 2012

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-008-5F2

DOI: 10.1007/978-3-642-31951-8_15

Abstract (EN): This work presents an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, an efficient method to learn temporal patterns in the form of sequences. XMuSer framework efficiency is grounded on a new coding methodology for temporal data and on the use of a predictive sequence miner. The frameworks selects and map the most interesting sequential patterns into a new table, the sequence relation. In the last step of our framework, we use an ILP algorithm to learn a classification theory on the enlarged relational database that consists of the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems and map each one of three different types of sequential patterns: frequent, closed or maximal. The experiments show that our ILP based framework gains both from the descriptive power of the ILP algorithms and the efficiency of the sequential miners. © 2012 Springer-Verlag Berlin Heidelberg.

Language: English

Type (Professor's evaluation): Scientific