English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This article presents the use of Answer Set Programming (ASP) to minesequential patterns. ASP is a high-level declarative logic programming paradigmfor high level encoding combinatorial and optimization problem solving as wellas knowledge representation and reasoning. Thus, ASP is a good candidate forimplementing pattern mining with background knowledge, which has been a datamining issue for a long time. We propose encodings of the classical sequentialpattern mining tasks within two representations of embeddings (fill-gaps vsskip-gaps) and for various kinds of patterns: frequent, constrained andcondensed. We compare the computational performance of these encodings witheach other to get a good insight into the efficiency of ASP encodings. Theresults show that the fill-gaps strategy is better on real problems due tolower memory consumption. Finally, compared to a constraint programmingapproach (CPSM), another declarative programming paradigm, our proposal showedcomparable performance.

Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks