Knowledge-based Sequence Mining with ASP
Overview & Synopsis
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.
Indexed from repository: HAL Open Science
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hal.science)
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Document Information
| Document Title | Knowledge-based Sequence Mining with ASP |
|---|---|
| Author / Organization | Martin Gebser, Thomas Guyet, René Quiniou, Javier Romero, Torsten Schaub |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2016 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-65097 |
| Original Source Link | Visit Source Page on hal.science » |