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Network Ranking Assisted Semantic Data Mining

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Bioinformatics and Biomedical Engineering (IWBBIO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

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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.

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Notes

  1. 1.

    The new variable needs to be ‘consumed’ by a literal to be added as a conjunction to this clause in the next step of rule refinement.

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Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Jan Kralj, Anže Vavpetič & Nada Lavrač

  2. Jožef Stefan International Postgraduate School, Jamova 39, 1000, Ljubljana, Slovenia

    Jan Kralj, Anže Vavpetič & Nada Lavrač

  3. University of Nova Gorica, Vipavska 13, 5000, Nova Gorica, Slovenia

    Nada Lavrač

  4. Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, USA

    Michel Dumontier

Authors
  1. Jan Kralj
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  2. Anže Vavpetič
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  3. Michel Dumontier
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  4. Nada Lavrač
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Corresponding author

Correspondence to Jan Kralj .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

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Kralj, J., Vavpetič, A., Dumontier, M., Lavrač, N. (2016). Network Ranking Assisted Semantic Data Mining. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_65

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_65

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