Knowledge-based Sequence Mining with ASP

Martin Gebser 1 Thomas Guyet 2 René Quiniou 2 Javier Romero 3 Torsten Schaub 2, 3
2 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : We introduce a framework for knowledge-based sequence mining, based on Answer Set Programming (ASP). We begin by modeling the basic task and refine it in the sequel in several ways. First, we show how easily condensed patterns can be extracted by modular extensions of the basic approach. Second, we illustrate how ASP's preference handling capacities can be exploited for mining patterns of interest. In doing so, we demonstrate the ease of incorporating knowledge into the ASP-based mining process. To assess the trade-off in effectiveness, we provide an empirical study comparing our approach with a related sequence mining mechanism.
Document type :
Conference papers
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download

https://hal.inria.fr/hal-01327363
Contributor : Thomas Guyet <>
Submitted on : Monday, June 6, 2016 - 4:20:42 PM
Last modification on : Monday, February 11, 2019 - 4:22:53 PM

File

Gebser_IJCAI2016.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01327363, version 1

Citation

Martin Gebser, Thomas Guyet, René Quiniou, Javier Romero, Torsten Schaub. Knowledge-based Sequence Mining with ASP. IJCAI 2016- 25th International joint conference on artificial intelligence, Jul 2016, New-york, United States. pp.8. ⟨hal-01327363⟩

Share

Metrics

Record views

666

Files downloads

414