In-close2, a high performance formal concept miner

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6828 LNAI, Issue , 2011, Pages 50-62

  Andrews, Simon ^a  

^a SHEFFIELD HALLAM UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PREPROCESSING TECHNIQUE; FORMAL CONCEPTS; FORMAL CONTEXTS; GENE EXPRESSION DATA; MINIMUM SUPPORT; OPTIMISATIONS; SOURCEFORGE;

GENE EXPRESSION; FORMAL CONCEPT ANALYSIS; INDIUM;

INDIUM; GENE EXPRESSION;

EID: 79961195003     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22688-5_4     Document Type: Conference Paper

References (16)

1. Andrews, S.: In-close, a fast algorithm for computing formal concepts. In: Rudolph, S., Dau, F., Kuznetsov, S.O. (eds.) ICCS 2009. CEUR WS, vol. 483 (2009), http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-483/
2. Andrews, S., Orphanides, C.: Analysis of large data sets using formal concept lattices. In: Kryszkiewicz, M., Obiedkov, S. (eds.) [10], pp. 104-115
3. Andrews, S., Orphanides, C.: Fcabedrock, a formal context creator. In: Croitoru, M., Ferré, S., Lukose, D. (eds.) ICCS 2010. LNCS, vol. 6208, pp. 181-184. Springer, Heidelberg (2010)
4. Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
5. Goethals, B.: Frequent itemset implementations (fimi) repository (2010), http://fimi.cs.helsinki.fi/
6. Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29(2), 147-160 (1950)
7. Kaytoue, M., Duplessis, S., Kuznetsov, S.O., Napoli, A.: Two fca-based methods for mining gene expression data. In: Ferré, S., Rudolph, S. (eds.) ICFCA 2009. LNCS (LNAI), vol. 5548, pp. 251-266. Springer, Heidelberg (2009)
8. Krajca, P., Outrata, J., Vychodil, V.: Parallel recursive algorithm for fca. In: Belohavlek, R., Kuznetsov, S.O. (eds.) CLA 2008 (2008)
9. Krajca, P., Vychodil, V., Outrata, J.: Advances in algorithms based on cbo. In: Kryszkiewicz, M., Obiedkov, S. (eds.) [10], pages 325-337
10. Kryszkiewicz, M., Obiedkov, S. (eds.): 7th International Conference on Concept Lattices and Their Applications, CLA 2010. University of Sevilla, Seville (2010)
11. Kuznetsov, S.O.: Learning of simple conceptual graphs from positive and negative examples. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 384-391. Springer, Heidelberg (1999)
12. Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generating concept lattices. Journal of Experimental and Theoretical Artificial Intelligence 14, 189-216 (2002)
13. Pensa, R.G., Boulicaut, J.-F.: Towards fault-tolerant formal concept analysis. In: Bandini, S., Manzoni, S. (eds.) AI*IA 2005. LNCS (LNAI), vol. 3673, pp. 212-223. Springer, Heidelberg (2005)
14. Priss, U.: Fca algorithms (2009), http://www.upriss.org.uk/fca/ fcaalgorithms.html
15. Tanabata, T., Sawase, K., Nobuhara, H., Bede, B.: Interactive data mining for image databases based on fca. Journal of Advanced Computational Intelligence and Intelligent Informatics 14(3), 303-308 (2010)
16. van der Merwe, D., Obiedkov, S.A., Kourie, D.G.: AddIntent: A new incremental algorithm for constructing concept lattices. In: Eklund, P.W. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 372-385. Springer, Heidelberg (2004)