Exploring multi-relational temporal databases with a propositional sequence miner

• Type: Article in International Scientific Journal
• Date: 2015

Authors

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

Journal

Title: Progress in Artificial Intelligence

Vol. 4 No. 1-2

Pages: 11-20

ISSN: 2192-6352

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00K-A5T

DOI: 10.1007/s13748-015-0065-x

Abstract (EN): In this work, we introduce the MuSer, a propositional framework that explores temporal information available in multi-relational databases. At the core of this system is an encoding technique that translates the temporal information into a propositional sequence of events. By using this technique, we are able to explore the temporal information using a propositional sequence miner. With this framework, we mine each class partition individually and we do not use classical aggregation strategies, like window aggregation. Moreover, in this system we combine feature selection and propositionalization techniques to cast a multi-relational classification problem into a propositional one. We empirically evaluate the MuSer framework using two real databases. The results show that mining each partition individually is a time- and memory-efficient strategy that generates a high number of highly discriminative patterns.

Language: English

Type (Professor's evaluation): Scientific