150
Views
1
CrossRef citations to date
0
Altmetric
Articles

Rough set-based approach for automated discovery of censored production rules

, &
Pages 151-166 | Received 20 Feb 2012, Accepted 24 Feb 2013, Published online: 14 Jun 2013

REFERENCES

  • Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM's Special Interest Group on Management of Data, 22(2), 805–810.
  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In J. B.Bocca, M.Jarke & C.Zaniolo (Eds.), Proceedings of the 20th international conference on very large databases (pp. 487–499). Santiago de Chile: Morgan Kaufmann.
  • Baqui, S., Just, J., & Baqui, S. C. (2009). Deriving strong association rules using a dependency criterion, the lift measure. International Journal of Data Analysis Techniques and Strategies, 1(3), 297–312.
  • Berzal, F., Blanco, I., Sanchez, D., & Vila, M. A. (2001). A new framework to assess association rules. Proceedings advances intelligent data analysis, 4th international symposiumLecture Notes in Computer Science, Vol. 2189, (pp. 95–104). Heidelberg and Berlin: Springer-Verlag.
  • Bharadwaj, K. K., & Jain, N. K. (1992). Hierarchical censored production rules (HAPRs) system. Data & Knowledge Engineering, 8, 19–34.
  • Bonikowski, Z., & Wybraniec-Skardowska, U. (2008). Vagueness and roughness. Transactions on rough sets-IX, Vol. 5390, (pp. 1–13). Heidelberg and Berlin: Springer-Verlag.
  • Bullard, L. A., Khoshgoftaar, T. M., & Gao, K. (2007). An application of a rule-based model in software quality classification. Proceedings of international conference on machine learning (ICML'2007) (pp. 204–210). Oregon State University, Corvallis, USA: Morgan Kaufmann.
  • Dubois, D., & Prade, H. (1990). Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems, 17, 191–209.
  • Emilyn, J., & Ramar, K. (2010). Rough set based clustering of gene expression data: A survey. International Journal of Engineering Science & Technology, 2(12), 7160–7164.
  • Fajriya Hakim, R. B., Seno, S., & Winarko, E. (2010). Clustering binary data based on rough set indiscernibility level. Biomedical Soft Computing and Human Sciences, 16(2), 87–95.
  • Gomolinska, A. (2010). Satisfiability judgement under incomplete information. Transactions on Rough Sets-XI, 5946, 66–91.
  • Grzymala-Busse, J. W. (2008). Three approaches to missing attribute values: A rough set perspective. Data mining: Foundations and practiceStudies in computational intelligence, Vol. 118, (pp. 139–152). Heidelberg and Berlin: Springer-Verlag.
  • Grzymala-Busse, J. W. (2010). Mining numerical data – a rough set approach. Transactions on Rough Sets-XI, 5946, 1–13.
  • Grzymala-Busse, J. W., & Grzymala-Busse, W. J. (2007). An experimental comparison of three rough set approaches to missing attribute values. Transactions on Rough Sets, 6, 31–50.
  • Grzymala-Busse, J. W., & Yao, Y. (2008). A comparison of the LERS classification system and rule management in PRSM. Proceedings of the sixth international conference on Rough Sets and Current Trends in Computing (RSCTC'2008) , Ohio, USALecture Notes in Artificial Intelligence, Vol. 5306, (pp. 139–152). Heidelberg and Berlin: Springer-Verlag.
  • Haddaway, P. (1987). A variable precision logic inference system employing the Dempster-Shafer uncertainty calculus (MS thesis (UILU-ENG-86-/777)). University of Illinois, Urbana- Champaign, IL, USA.
  • Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation. Data Mining and Knowledge Discovery, 8, 53–87.
  • Hassanien, A. E., Suraj, Z., Slezak, D., & Lingras, P. (2008). Rough computing. Theories, technologies, and applicationsSeries: in memoriam Professor Zdzislaw Pawlak. New York: IGI Global Hershey.
  • Herawan, T., Ghazali, R., Yanto, I. T. R., & Deris, M. M. (2010). Rough set approach for categorical data clustering. International Journal of Database Theory and Application, 3(1), 33–52.
  • Hung, Y. H. (2008). Improving classification accuracy of IC packaging products database based on variable precision rough sets. Information Technology Journal, 7(3), 440–449.
  • Jaworski, W. (2008). Rule induction: Combining rough set and statistical approaches. Proceedings of the sixth international conference on Rough Sets and Current Trends in Computing (RSCTC'2008) Ohio, USALecture Notes in Artificial Intelligence, Vol. 5306, (pp. 170–180)
  • Kryszkiewicz, M. (1998). Rough set approach to incomplete information systems. Journal of Information Sciences, 112, 39–49.
  • Li, D., & Zhang, W. (2006). Gene selection using rough set theory. Proceedings of 1st international conference on Rough Sets and Knowledge Technology (RSKT 2006) (pp. 778–785). Chongqing: Springer-Verlag.
  • Li, J., & Cercone, N. (2005). Discovering and ranking important rules. Proceedings of IEEE international conference on granular computing (pp. 506–511). Beijing,China: IEEE Computer Society.
  • Li, J., & Cercone, N. (2006). Introducing a rule importance measure. Transactions on Rough Sets, 5, 167–189.
  • Li, J., Pattaraintakorn, P., & Cercone, V. (2007). Rule evaluations, attributes, and rough sets: Extension and a case study. Transactions on rough sets VI: Commemorating life and work of Zdislaw Pawlak, Part ILNCS, Vol. 4374, (pp. 152–171). Berlin, Heidelberg: Springer-Verlag.
  • Liang, J., & Xu, Z. (2002). The algorithm on knowledge reduction in incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(1), 95–103.
  • Lingras, P. J., & Yao, Y. (1998). Data mining using extensions of the rough set model. Journal of American Society for Information Science, 49, 415–422.
  • Liu, D., Hu, P., & Jiang, C. (2008). The incremental learning methodology of VPRS based on complete information system. Proceedings of the third international conference on Rough Sets and Knowledge Technology (RSKT'2008) Chengdu, ChinaLecture Notes in Artificial Intelligence, Vol. 5009, (pp. 276–283). Heidelberg and Berlin: Springer-Verlag.
  • Mahajan, P., Kandwal, R., & Vijay, R. (2011). General framework for cluster based active learning algorithm. International Journal on Computer Science and Engineering (IJCSE), 3(1), 307–312.
  • Michalski, R. S., & Winston, P. H. (1986). Variable precision logic. Artificial Intelligence, 29, 121–146.
  • Nguyen, H. S. (2002). Scalable classification method based on rough sets. Proceedings of Rough Sets and Current Trends in Computing (RSCTC 2002)Lecture Notes in Artificial Intelligence, Vol. 2475, (pp. 433–440). Malvern, PA: Springer.
  • Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 2, 341–356.
  • Pawlak, Z. (1985). Rough sets and fuzzy sets. Fuzzy Sets and Systems, 17, 99–102.
  • Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Boston, MA: Kluwer Academic.
  • Pawlak, Z., Grzymala-Busse, J. W., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of ACM, 38(11), 89–95.
  • Polkowski, L. (2003). Rough sets', mathematical foundation. Heidelberg: Physica-Verlag.
  • Predki, B., Slowinski, R., Stefanowski, J., Susmaga, R., & Wilk, Sz. (1998). ROSE. Software implementation of the rough set theory. Lecture Notes in Computer Science, 1424, 605–608.
  • Shen, H., Yang, S., & Liu, J. (2010). On attribute reduction of rough set based on pruning rules. Proceedings of the 5th international conference on Rough Set and Knowledge Technology (RSKT'10) (pp. 86–93). Berlin and Heidelberg: Springer-Verlag.
  • Skowron, A., & Stepaniuk, J. (1992). Intelligent systems based on rough set approach. Proceedings of the international workshop rough sets: State of the art and perspectives Extended Abstracts (pp. 62–64). Poland: Kiekrz-Poznan.
  • Skoworon, A., & Stepanuik, J. (1995). Generalized approximation spaces. In T. Y.Lin & A. M.Wildberger (Eds.), Soft computing (pp. 18–21). San Diego, CA: The Society for Computer Simulation.
  • Slezak, D., & Widz, S. (2010). Is it important which rough-set-based classifier extraction and voting criteria are applied together? In M.Szczuka et al. (Eds.), RSCTC 2010, LNAI, 6086 (pp. 187–196). Berlin and Heidelberg: Springer-Verlag.
  • Slezak, D., & Widz, S. (2011). Rough-set-inspired feature subset selection, classifier construction, and rule aggregation. Proceedings of the 6th international conference on Rough Sets and Knowledge Technology (RSKT 2011) (pp. 81–88). Berlin and Heidelberg: Springer-Verlag.
  • Slowinski, R. (Ed.). (1992). Intelligent decision support: Handbook of applications and advances of the rough sets theory. Boston, MA: Kluwer Academic.
  • Tsumoto, S. (2011a). A new framework for incremental rule induction based on rough sets. Proceedings of IEEE international conference on granular computing (pp. 681–686). Kaohsiung: IEEE Computer Society.
  • Tsumoto, S. (2011b). Incremental rule induction based on rough set theory. Foundations of intelligent systems – 19th international symposium. Warsaw: Springer.
  • Tsumoto, S. (2011c). Rule induction methods with hierarchical sampling. Proceedings of the 10th IEEE international conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011 (pp. 193–202). Banff: IEEE Computer Society.
  • Yang, L., & Yang, L. (2006). Study of cluster algorithm based on rough sets theory. Proceeding of the sixth international conference on Intelligent System Design & Application (ISDA'06) (pp. 492–496). Washington: IEEE Computer Society.
  • Yao, Y. Y. (1995). On combining rough and fuzzy sets. In T. Y.Lin (Ed.), Proceedings of the CSC'95 workshop on rough sets and database mining (pp. 165–172). Nashville, Tennessee, USA: San Jose State University.
  • Yao, Y. Y. (2007). A note on definability and approximations. Transactions on Rough Sets VII, Lecture Notes in Computer Science, 4400, 274–282.
  • Yao, Y. Y. (2009). Interpreting concept learning in cognitive informatics and granular computing. IEEE Transactions on System, Man and Cybernetics, B, 39, 855–866.
  • Yao, Y. Y. (2010). Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353.
  • Yao, Y. Y., Li, X., Lin, T. Y., & Liu, Q. (1995). Representation and classification of rough set models. Soft computing: Rough sets, fuzzy logic, neural network, uncertainty management and knowledge discovery (pp. 44–47), San Diego, CA: Society of Computer Simulation.
  • Yao, Y. Y., Wong, S. K. M., & Lin, T. Y. (1997). A review of rough set models'. In T. Y.Lin & N.Cercone (Eds.), Proceedings of rough sets and data mining: Analysis for imprecise data (pp. 47–75). Boston, MA: Kluwer Academic.
  • Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge Data Engineering, 12(3), 372–390.
  • Ziarko, W. P. (Ed.). (1994). Rough sets, fuzzy sets and knowledge discovery (RSKD'93), workshops in computing. London and Berlin: Springer-Verlag and British Computer Society.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.