Network Ranking Assisted Semantic Data Mining View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Jan Kralj , Anže Vavpetič , Michel Dumontier , Nada Lavrač

ABSTRACT

Semantic data mining (SDM) uses annotated data and interconnected background knowledge to generate rules that are easily interpreted by the end user. However, the complexity of SDM algorithms is high, resulting in long running times even when applied to relatively small data sets. On the other hand, network analysis algorithms are among the most scalable data mining algorithms. This paper proposes an effective SDM approach that combines semantic data mining and network analysis. The proposed approach uses network analysis to extract the most relevant part of the interconnected background knowledge, and then applies a semantic data mining algorithm on the pruned background knowledge. The application on acute lymphoblastic leukemia data set demonstrates that the approach is well motivated, is more efficient and results in rules that are comparable or better than the rules obtained by applying the incorporated SDM algorithm without network reduction in data preprocessing. More... »

PAGES

752-764

References to SciGraph publications

Book

TITLE

Bioinformatics and Biomedical Engineering

ISBN

978-3-319-31743-4
978-3-319-31744-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-31744-1_65

DOI

http://dx.doi.org/10.1007/978-3-319-31744-1_65

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013043325


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