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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.

References

  1. Adhikari, P.R., Vavpetič, A., Kralj, J., Lavrač, N., Hollmén, J.: Explaining mixture models through semantic pattern mining and banded matrix visualization. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS, vol. 8777, pp. 1–12. Springer, Heidelberg (2014)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)

    Google Scholar 

  3. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)

    Article  Google Scholar 

  4. Bavelas, A.: Communication patterns in task-oriented groups. J. Acoust. Soc. Am. 22, 723–730 (1950)

    Article  Google Scholar 

  5. Consortium, G.O.: The gene ontology project in 2008. Nucleic Acids Res. 36(Database–Issue), 440–444 (2008)

    Google Scholar 

  6. Fisher, R.A.: On the interpretation of \(\chi ^{2}\) from contingency tables, and the calculation of P. J. Roy. Stat. Soc. 85(1), 87–94 (1922)

    Article  Google Scholar 

  7. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)

    Article  Google Scholar 

  8. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)

    Article  Google Scholar 

  9. Hämäläinen, W.: Efficient search for statistically significant dependency rules in binary data. Ph.D. thesis, Department of Computer Science, University of Helsinki, Finland (2010)

    Google Scholar 

  10. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)

    MathSciNet  MATH  Google Scholar 

  11. Huang, D.W., Sherman, B.T., Lempicki, R.A.: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4(1), 44–57 (2008)

    Article  Google Scholar 

  12. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  13. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  14. Klösgen, W.: Explora: a multipattern and multistrategy discovery assistant. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 249–271. American Association for Artificial Intelligence, Menlo Park (1996)

    Google Scholar 

  15. Lavrač, N., Kavšek, B., Flach, P.A., Todorovski, L.: Subgroup discovery with CN2-SD. J. Mach. Learn. Res. 5, 153–188 (2004)

    MathSciNet  Google Scholar 

  16. Ławrynowicz, A., Potoniec, J.: Fr-ONT: an algorithm for frequent concept mining with formal ontologies. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 428–437. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data mining (KDD 1998), pp. 80–86. AAAI Press (1998)

    Google Scholar 

  18. Maglott, D., Ostell, J., Pruitt, K.D., Tatusova, T.: Entrez gene: gene-centered information at NCBI. Nucleic Acids Res. 33(Database issue), D54–D58 (2005)

    Article  Google Scholar 

  19. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M.: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27(1), 29–34 (1999)

    Article  Google Scholar 

  20. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  21. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press, Cambridge (1991)

    Google Scholar 

  22. Podpečan, V., Lavrač, N., Mozetič, I., Novak, P.K., Trajkovski, I., Langohr, L., Kulovesi, K., Toivonen, H., Petek, M., Motaln, H., et al.: SegMine workflows for semantic microarray data analysis in Orange4WS. BMC Bioinformatics 12(1), 416 (2011)

    Article  Google Scholar 

  23. Srinivasan, A.: Aleph Manual (2007)

    Google Scholar 

  24. Trajkovski, I., Lavrač, N., Tolar, J.: SEGS: search for enriched gene sets in microarray data. J. Biomed. Inform. 41(4), 588–601 (2008a)

    Article  Google Scholar 

  25. Trajkovski, I., Železný, F., Lavrač, N., Tolar, J.: Learning relational descriptions of differentially expressed gene groups. IEEE Trans. Syst. Man Cybern. Part C 38(1), 16–25 (2008b)

    Article  Google Scholar 

  26. Vavpetič, A., Lavrač, N.: Semantic subgroup discovery systems and workflows in the SDM-toolkit. Comput. J. 56(3), 304–320 (2013)

    Article  Google Scholar 

  27. Vavpetič, A., Novak, P.K., Grčar, M., Mozetič, I., Lavrač, N.: Semantic data mining of financial news articles. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds.) DS 2013. LNCS, vol. 8140, pp. 294–307. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  28. Žáková, M., Železný, F., Garcia-Sedano, J.A., Masia Tissot, C., Lavrač, N., Křemen, P., Molina, J.: Relational data mining applied to virtual engineering of product designs. In: Muggleton, S.H., Otero, R., Tamaddoni-Nezhad, A. (eds.) ILP 2006. LNCS (LNAI), vol. 4455, pp. 439–453. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  29. Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  30. Xing, W., Ghorbani, A.: Weighted pagerank algorithm. In: 2nd Annual Conference on Communication Networks and Services Research, pp. 305–314. IEEE (2004)

    Google Scholar 

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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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