Improvement of FP-Growth Algorithm for Mining Description-Oriented Rules

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)

Abstract

In the paper new modification of the rules induction method for description of gene groups using Gene Ontology based on FP-growth algorithm is proposed. The modification takes advantage of the hierarchical structure of GO graph, specific property of a single prefix-path FP tree and the fact that if we generate rules for description purposes we do not include into rule premise two GO terms that are in parent-children relation. The proposed algorithms was implemented and tested with two different expression datasets. Time performance of old and new approach is compared together with descriptions obtained with two methods. The results show that the new method allows generating rules faster, while the number of rules and coverage is similar in both approaches.

Keywords

rules induction FP-growth Gene Ontology time performance functional description 

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of 20th International Conference on Very Large Data Bases (VLDB 1994), pp. 487–499. Morgan Kaufmann Publishers Inc. (1994)Google Scholar
  2. 2.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., et al.: Gene Ontology: tool for the unification of biology. Nature Genetics 25(1), 25–29 (2000)CrossRefGoogle Scholar
  3. 3.
    Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., et al.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2(1), 65–73 (1998)CrossRefGoogle Scholar
  4. 4.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 95(25), 14,863–14,868 (1998)Google Scholar
  5. 5.
    Gruca, A., Sikora, M., Polański, A.: RuleGO: a logical rules-based tool for description of gene groups by means of gene ontology. Nucleic Acids Research 39(suppl. 2), W293–W301 (2011)Google Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., et al.: The transcriptional program in the response of human fibroblasts to serum. Science 283(5398), 83–87 (1999)CrossRefGoogle Scholar
  8. 8.
    Sikora, M., Gruca, A.: Induction and selection of the most interesting gene ontology based multiattribute rules for descriptions of gene groups. Pattern Recognition Letters 32(2), 258–269 (2011)CrossRefGoogle Scholar
  9. 9.
    Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. International Journal of Intelligent Systems 16(1), 13–27 (2001)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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