Knowledge discovery through creating formal contexts

Article, Part of book or chapter of book English OPEN
Andrews, Simon ; Orphanides, Constantinos (2010)

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called formal concept analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. This paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Examples are given of how formal contexts can be created using FcaBedrock and then analysed for knowledge discovery, using real datasets. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large datasets are easily converted into a standard FCA format.
  • References (13)
    13 references, page 1 of 2

    Abadi, D.J., Marcus, A., Madden, S.R. and Hollenbach, K. (2009) SW-Store: a vertically partitioned DBMS for Semantic Web data management. In: VLDB, vol. 18. Springer-Verlag. pp. 385-406.

    Andrews, S. (2011) In-Close2, a High Performance Formal Concept Miner. In: Proceedings of the 19th International Conference on Conceptual Structures (ICCS) 2011. LNAI 6828. Berlin: Springer-Verlag. pp. 50-62.

    Andrews, S. (2009a) Data Conversion and Interoperability for FCA. In: CSTIW 2009, http://www.kde.cs.uni-kassel.de/ws/cs-tiw2009/proceedings\ _final\_15July.pdf. pp. 42-49.

    Andrews S. (2009b) In-Close, a Fast Algorithm for Computing Formal Concepts. In: Rudolph, Dau, Kuznetsov (Eds.): Supplementary Proceedings of ICCS'09, CEUR WS 483.

    Andrews, S. and Orphanides, C. (2010a) FcaBedrock, a Formal Context Creator. In: Croitoru, M., Ferre, S. and Lukose, D. (eds.) Conceptual Structures: From Information to Intelligence, Proceedings of the 18th International Conference on Conceptual Structures (ICCS) 2010. LNAI 6208. Berlin: Springer. pp. 181-184.

    Andrews, S. and Orphanides, C. (2010b) Knowledge Discovery through Creating Formal Contexts. In: Hill, R. (ed.) First International Workshop on Computational Intelligence in Networks and Systems (CINS 2010), in Xhafa, F., Demetriadis, S., Caballe, S., Abraham, A. (eds.) Second International Conference on Intelligent Networking and Collaborative Systems (INCoS) 2010. ISBN: 978-0-7695-4278-2/10. DOI 10.1109/INCOS.2010.53. IEEE Computer Society. pp. 455-460.

    Arevalo, G., Berry, A., Huchard, M., Perrot, G., Sigayret, A. (2007) Performances of Galois Sub-hierarchy-building algorithms. In: Kuznetsov, S.O., Schmidt, S. (eds), ICFCA 2007, LNAI 4390. Berlin: Springer-Verlag. pp. 166-180.

    Boulicaut, J-F. and Besson, J. (2008) Actionability and Formal Concepts: A Data Mining Perspective. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933. Berlin/Heidelberg: Springer-Verlag. pp. 14-31.

    Frank, A. and Asuncion, A. (2010) UCI Machine Learning Repository. [http:// archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

    Ganter, B. (1984) Two Basic Algorithms in Concept Analysis. Technical Report FB4- Preprint No. 831, TH Marstadt.

  • Software (3)
  • Metrics
    No metrics available
Share - Bookmark