Network ranking assisted semantic data mining

J. Kralj*, A. Vavpetič, M. Dumontier, N. Lavrač

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


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.
Original languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering
Subtitle of host publication4th International Conference, IWBBIO 2016, Granada, Spain, April 20-22, 2016, Proceedings
Number of pages13
Publication statusPublished - 2016
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science


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