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A Graph-Path Counting Approach for Learning Head Output Connected Relations

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Computational Collective Intelligence (ICCCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9876))

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Abstract

Concept discovery is a multi-relational data mining task where the problem is inducing definitions of a relation in terms of other relations. In this paper, we propose a graph-based concept discovery system to learn definitions of head output connected relations. It inputs the data in relational format, converts it into graphs, and induces concept definitions from graphs’ paths. The proposed method can handle n-ary relations and induce recursive concept definitions. Path frequencies are used to calculate the quality of the induced concept descriptors. The experimental results show that results obtained are comparable to those reported in literature in terms of running time and coverage; and is superior over some methods as it can induce shorter concept descriptors with the same coverage.

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References

  1. Dzeroski, S.: Multi-relational data mining: an introduction. SIGKDD Explor. 5(1), 1–16 (2003)

    Article  MathSciNet  Google Scholar 

  2. Muggleton, S.: Inductive logic programming. New Gener. Comput. 8(4), 295–318 (1991)

    Article  MATH  Google Scholar 

  3. Bratko, I., Muggleton, S.: Applications of inductive logic programming. Communications of the ACM 38(11), 65–70 (1995)

    Article  Google Scholar 

  4. Richards, B.L., Mooney, R.J.: Learning relations by pathfinding. In: Proceedings of the 10th National Conference on Artificial Intelligence, San Jose, CA, 12–16 July 1992, pp. 50–55 (1992)

    Google Scholar 

  5. Gao, Z., Zhang, Z., Huang, Z.: Extensions to the relational paths based learning approach RPBL. In: First Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009, Dong hoi, Quang binh, Vietnam, 1–3 April 2009, pp. 214–219 (2009)

    Google Scholar 

  6. Abay, N.C., Mutlu, A., Karagoz, P.: A graph-based concept discovery method for n-Ary relations. In: Madria, S., Hara, T. (eds.) DaWaK 2015. LNCS, vol. 9263, pp. 391–402. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  7. Gonzalez, J., Holder, L., Cook, D.J.: Application of graph-based concept learning to the predictive toxicology domain. In: Proceedings of the Predictive Toxicology Challenge Workshop (2001)

    Google Scholar 

  8. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 9–12 December 2002, Maebashi City, Japan, pp. 721–724 (2002)

    Google Scholar 

  9. Yoshida, K., Motoda, H., Indurkhya, N.: Graph-based induction as a unified learning framework. Appl. Intell. 4(3), 297–316 (1994)

    Article  Google Scholar 

  10. Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  11. Santos, J.C.A., Tamaddoni-Nezhad, A., Muggleton, S.: An ILP system for learning head output connected predicates. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS, vol. 5816, pp. 150–159. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  12. Hinton, G.: UCI machine learning repository (1990)

    Google Scholar 

  13. Kavurucu, Y., Senkul, P., Toroslu, I.H.: Concept discovery on relational databases: new techniques for search space pruning and rule quality improvement. Knowl.-Based Syst. 23(8), 743–756 (2010)

    Article  Google Scholar 

  14. Mutlu, A., Karagoz, P.: A hybrid graph-based method for concept rule discovery. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 327–338. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Computer Engineering Department, Kocaeli University, Kocaeli, Turkey

    Nuran Peker & Alev Mutlu

Authors
  1. Nuran Peker
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  2. Alev Mutlu
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Corresponding author

Correspondence to Alev Mutlu .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology , Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Aristotle University of Thessaloniki , Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Manageme, Democritus University of Thrace Department of Forestry and Manageme, Orestiada Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology , Wrocław, Poland

    Bogdan Trawiński

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Peker, N., Mutlu, A. (2016). A Graph-Path Counting Approach for Learning Head Output Connected Relations. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-45246-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45245-6

  • Online ISBN: 978-3-319-45246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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