Abstract
This paper presents evaluation of different types of Binary Decision Diagrams (BDDs) applied to Formal Concept Analysis (FCA). The aim is to increase the FCA capability to handle large formal contexts and perform faster operations over different types of this data structure. The main idea is to represent formal context using BDDs for later extraction of the set of all formal concepts from this implicit representation. A comparison of a concept extraction algorithm using contexts implemented as table and BDD are presented. BDD is evaluated over two different implementation libraries, BuDDy and CUDD. A ZBDDs (Zero-Supressed BDDs) version of the concepts extraction algorithm is also provided. BDD has been evaluated based on several types of randomly generated synthetic contexts with large amounts of objects. Contexts are evaluated according to the computational time complexity required to build and extract the set of all concepts from it. In this work, it is shown that BDD could be used to deal with large formal contexts especially when those have few attributes and many objects. To overcome the limitations of having contexts with fewer attributes, one could consider vertical partitions of the context to be used with distributed FCA algorithms based on BDD.
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Li, Y., Liu, Z.T., Shen, X.J., Wu, Q., Qiang, Y.: Theoretical research on the distributed construction of concept lattices. In: International Conference on Machine Learning and Cybernetics, vol. 1, pp. 474–479 (2003)
Liu, Z., Li, L., Zhang, Q.: Research on a union algorithm of multiple concept lattices. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 533–540. Springer, Heidelberg (2003)
Lévy, G., Baklouti, F.: A distributed version of the ganter algorithm for general galois lattices (2005)
Bryant, R.: Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers C-35(8), 677–691 (1986)
Yevtushenko, S.: Computing and visualizing concept lattices. PhD thesis, TU Darmstadt, Fachbereich Informatik (2004)
Lind-Nielsen, J.: Buddy: A binary decision diagram. Technical report, Department of Information Technology, Technical University of Denmark, Lyngby, Denmark (1999), http://www.itu.dk/research/buddy
Somenzi, F.: CUDD: CU decision diagram package release (1998)
Minato, S.: Zero-suppressed BDDs for set manipulation in combinatorial problems. In: DAC 1993: Proceedings of the 30th International Conference on Design Automation, pp. 272–277. ACM, New York (1993)
Grätzer, G.: General Lattice Theory. Birkhäuser, Basel (1978)
Carpineto, C., Romano, G.: Concept Data Analysis: Theory and Applications. John Wiley & Sons, Indianapolis (2004)
Zaki, M., Hiao, C.: ChARM: An efficient algorithm for closed association rule mining. Technical Report 99-10, Computer Science Dept., Rensselaer Polytechnic Inst., Troy, NY, USA (October 1999)
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Rimsa, A., Zárate, L.E., Song, M.A.J. (2009). Evaluation of Different BDD Libraries to Extract Concepts in FCA – Perspectives and Limitations. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2009. Lecture Notes in Computer Science, vol 5544. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01970-8_36
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DOI: https://doi.org/10.1007/978-3-642-01970-8_36
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