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Rough Set Approach in Machine Learning: A Review

by Prerna Mahajan, Rekha Kandwal, Ritu Vijay
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 10
Year of Publication: 2012
Authors: Prerna Mahajan, Rekha Kandwal, Ritu Vijay
10.5120/8924-2996

Prerna Mahajan, Rekha Kandwal, Ritu Vijay . Rough Set Approach in Machine Learning: A Review. International Journal of Computer Applications. 56, 10 ( October 2012), 1-13. DOI=10.5120/8924-2996

@article{ 10.5120/8924-2996,
author = { Prerna Mahajan, Rekha Kandwal, Ritu Vijay },
title = { Rough Set Approach in Machine Learning: A Review },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 10 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number10/8924-2996/ },
doi = { 10.5120/8924-2996 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:27.017979+05:30
%A Prerna Mahajan
%A Rekha Kandwal
%A Ritu Vijay
%T Rough Set Approach in Machine Learning: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 10
%P 1-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction.

References
  1. Agrawal, R. , Imielinski, T. and Swami, A. Mining association rules between sets of items in large databases. in Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data,( Washington, D. C,1993), ACM Press ,22(2), 805-810.
  2. Agrawal, R. and Srikant, R. Fast algorithms for mining association rules in large databases. in J. B. Bocca, M. Jarke, and C. Zaniolo,(Eds. ), Proceedings of the 20th International Conference on Very Large Data Bases, VLDB,( Santiago, Chile ,1994), Morgan Kaufmann , 487-499.
  3. Ahn,B. S. , Cho, S. S. and Kim C. Y. The integrated methodology of rough set theory and artificial neural networks for business failure predictions. Expert Systems with Applications, 18, 2,(2000),65-74.
  4. Almuallim,H. and Dietterich,T. G. Learning with many irrelevant features. in Proceedings of the 9th National Conference on Artificial Intelligence, ( Anaheim, California ,1991), AAAI Press,547-552.
  5. Al-Maqaleh, B. M. and Bharadwaj, K. K. Genetic programming approach to hierarchical production rule discovery. Transactions on Engineering, Computing and Technology,6(2005), 271–274.
  6. Arco, L. , Bello, R. , Caballero, Y. and Falcon, R. Rough Text Assisting Text Mining: Focus on Documents Clustering Validity. In R. Bello,R. Falc N , W. Pedrycz (Eds. ) Granular Computing at the Junction of Rough Sets and Fuzzy Sets , (Heidelberg, 2008) , Springer-Verlag ,229-248.
  7. Asharaf, S. , Shevade, S. K. and Murty, N. M. Rough support vector clustering. Pattern Recognition, 38(2005), 1779–1783.
  8. Asharaf, S. , Shevade, S. K. and Murty, N. M. Rough set based incremental clustering of interval data, Pattern Recognition Letters, 27 (2006), 515-519.
  9. Baqui,S. ,Just,J. and Baqui,S. C. Deriving strong association rules using a dependency criterion, the lift measure. International Journal of Data Analysis. Technical Strategy, 1,3(2009),297–312.
  10. Bazan, J. , Skowron, A. and Synak, P. Dynamic reducts as a tool for extracting laws from decision tables. in Proceedings of the Symposium on Methodologies for Intelligent Systems, Lecture Notes in Artificial Intelligence-869, (Berlin ,1994),Springer-Verlag,346-355.
  11. Bazan, J. A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision system. In Polkowski, L. , Skowron, A. Eds. Rough Sets in Knowledge Discovery , Physica-Verlag, Heidelberg ,1998, 321–365.
  12. Bazan, J. G. , Peters, J. F. , Skowron, A. and Nguyen, H. S. Rough set approach to pattern extraction from classifiers. Electronic Notes in Theoretical Computer Science, 82,4(2003),1–10.
  13. Bell, D. and Guan, J. Computational methods for rough classification and discovery. Journal of the American Society for Information Science, 49,5(1998), 403-414.
  14. Berzal,F. ,Blanco,I. ,Sanchez,D. and Vila,M. A. A new framework to assess association rules. in Symposiom on Intelligent Data Analysis, Lecture Notes in Computer Sciences. 2189(2001), 95–104.
  15. Beynon, M. J. and Peel, M. J. Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega, International Journal of Management Science, 29(2001), 561–576.
  16. Bharadwaj, K. K. and Varshneya, R. Parallelization of hierarchical censored production rules. Information and Software Technology, 37,8(1995),453-460.
  17. Bharadwaj, K. K. and Al-Maqaleh, B. M. Evolutionary approach for automated discovery of censored production rules. Enformatika, 10(2005), 147–152.
  18. Bharadwaj, K. K. and Al-Maqaleh, B. M. Evolutionary approach for automated discovery of censored production rules with fuzzy hierarchy. In Proceedings of the International Multi-Conference of Engineers and Computer Scientists(Hong Kong,2007), Newswood Limited ,716-721.
  19. Bharadwaj, K. K. and Ratnoo,S. Parallel Genetic algorithm approach to automated discovery of hierarchical production rules. In J. Mehnen (Ed. ) ,World online international conference on soft computing and applications (WSC 2008), Applications of Soft Computing, Springer, Berlin/Heidelberg , 2008,505-408.
  20. Bharadwaj,K. K. and Ratnoo,S. A parallel genetic programming based intelligent miner for discovery of censored production rules with fuzzy hierarchy. Expert System and Applications, 37,6(2010), 4601-4610.
  21. Bian, X. Certain rule learning of the inconsistent data. Journal of East China Shipbuilding Institute,12,1(1998),25–30.
  22. Blaszczynski, J. and Slowinski, R. Incremental induction of decision rules from dominance-based rough approximations. Electronic Notes in Theoretical Computer Science, 82,4(2003),1–12.
  23. Bonikowski, Z. and Wybraniec-Skardowska, U. Vagueness and Roughness. Transactions on Rough Sets-IX,5390, Springer-Verlag ,Heidelberg - Berlin, Germany,2008,1-13.
  24. Caballero,Y. , Bello,R. , Alvarez,D. and García,M. M. Two new feature selection algorithms with Rough Sets Theory, in Bramer, M. (ed. ). Artificial Intelligence in Theory and Practice , Springer, Santiago, Chile ,2006,209-216.
  25. Carlin, U. , Komorowski,J. and Ohrn, A. Rough set analysis of medical datasets and A case of patient with suspected acute appendicitis. European Conference of Artificial Intelligence(ECAI 98) ,Workshop on Intelligent data analysis in medicine and pharmacology, (Chichester, 1998),John Wiley and Sons,18-28.
  26. Caruana, R. and Freitag, D. Greedy attribute selection. in Proceedings of the 11th International Conference on Machine Learning,( NJ, USA,1994) ,Morgan Kaufmann,28-36.
  27. Chen, C. Y. , Hwang, S. C. and Oyang, Y. J. A statistics-based approach to control the quality of subclusters in incremental gravitational clustering. Pattern Recognition, 38(2005), 2256–2269.
  28. Chen,D. , Cui, D. W. ,Wang, C. X. and Wang, Z. R. A Rough Set-Based Hierarchical Clustering Algorithm for Categorical Data. International Journal of Information Technology, 12,3(2006),149-159.
  29. Choubey,S. K. , Deogun, J. S. , Raghavan, V. V. and Sever, H. A comparison of feature selection algorithms in the context of rough classifiers. in Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, (New Orleans, LA, USA,1996),IEEE Conference Publications,1122-1128.
  30. Deogun, J. S. , Raghavan, V. V. and Sever, H. Exploiting upper approximations in the rough set methodology. In U. M. Fayyad and R. Uthurusamy(Eds. ), Proceedings of First International Conference on Knowledge Discovery and Data Mining, (Canada, 1995),AAAI Press,69-74.
  31. Deogun, J. S. , Choubey, S. K. , Raghavan, V. V. and Sever, H. Feature selection and effective classifiers. Journal of the American Society for Information Science, 49, 5 (1998), 423-434.
  32. Do Prado H. A, Engel ,P. M. and Filho, H. C. Rough clustering: an alternative to find meaningful clusters by using the reducts from a dataset. In J. Alpigini, J. Peters, A. Skowron, N. Zhong(Eds. )Proceedings of the Rough Sets and Current Trends in Computing (RSCTC'02), Lecture Notes in Artificial Intelligence, LNAI- 2475, (Heidelberg, 2002), Springer-Verlag, 234–238.
  33. Dong,J. Z. Zhong,N. and Ohsuga,S. Using rough sets with heuristics to feature selection. In N. Zhong, A. Skowron, and S. Ohsuga(Eds. ), Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC-99), Lecture Notes in Artificial Intelligence, Berlin,LNAI- 1711, (Heidelberg, 1999), Springer-Verlag, 178–187.
  34. Dubois, D. and Prade, H. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems, 17(1990), 191-209.
  35. Duda , R. , Hart , P. and Stork , D. Pattern classification. 2 nd edition . John Wiley & Sons, New York, 2001 .
  36. Duentsch, I. and Gediga, G. Statistical evaluation of rough set dependency analysis. International Journal of Human-Computer Studies, 46(1997), 589–604.
  37. Emilyn, J. and Ramar, K. Rough Set Based Clustering Of Gene Expression Data: A Survey . International Journal of Engineering Science and Technology,2,12(2010),7160-7164.
  38. Fong, J. , Wong, H. K. and Huang, S. M. Relevance feedback icon continuous and incremental data mining association rules using frame metadata model. Knowledge-Based Systems, 16(2003), 91–100.
  39. Grzymala-Busse ,J. . LERS A system for learning from examples based on rough sets. Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory , (Dordrecht, 1992) , Kluwer,3-18.
  40. Grzymala-Busse,J. and Grzymala-Busse,W. An experimental comparison of three rough set approaches to missing attribute values. Transactions on Rough Sets, Lecture Notes in Computer Science,LNCS, 6(2007),31-50.
  41. Grzymala-Busse, J. W. Three Approaches to Missing Attribute Values: A Rough Set Perspective. Data Mining: Foundations and Practice , Studies in Computational Intelligence,vol. 118, Springer-Verlag ,Heidelberg , Germany , 2008,139—152.
  42. Grzymala-Busse J. W. Mining Numerical Data - A Rough Set Approach . Transactions on Rough Sets-XI, 5946(2010), 1--13.
  43. Guo, S. , Wang, Z. Y. , Wu, Z. C. and Yan, H. P. A novel dynamic incremental rules extraction algorithm based on rough set theory. in Proceedings of the fourth International Conference on Machine learning and cybernetics, (Guangzhou, 2005), IEEE Computer Society,18-21.
  44. Hakim,F. , Winarko,S. and Winarko,E. Clustering Binary Data Based on Rough Set Indiscernibility Level. Biomedical Soft Computing and Human Sciences,16,2 (2010),87-95.
  45. Han,J. ,Pei,J. ,Yin,Y. and Mao,R . Mining frequent patterns without candidate generation. Data Mining and Knowledge Discovery, 8(2004),53–87.
  46. Hassanien ,A. E. , Suraj Z. , Slezak, D. and Lingras, P. Rough Computing. Theories, Technologies, and Applications, series: In memoriam Professor Zdzislaw Pawlak, IGI Global Hershey ,New York,2008.
  47. Herawan ,T. , Ghazali,R. , Yanto, I. T. R. and Deris, M. M. Rough Set Approach for Categorical Data Clustering. International Journal of Database Theory and Application, 3,1(2010), 33-52.
  48. Herawan, T. , Yanto, I. and Deris, M. . ROSMAN: ROugh Set approach for clustering Supplier base MANagement. Biomedical Soft Computing and Human Sciences, 162(2010),105-114.
  49. Hirano, S. and Tsumoto, S. Rough clustering and its application to medicine. Information Sciences, 124(2000), 125–137.
  50. Hu, X. Knowledge discovery in databases: An attribute-oriented rough set approach. Ph. D. Thesis, Regina University,1995.
  51. Hu, K. Y. , Lu, Y. C. and Shi, C. Y. Feature ranking in rough sets. Artificial Intelligence Communications, 16,1(2003),41–50.
  52. Huang, C. M. , Hsu, J. M. , Lai, H. Y. , Huang, D. T. and Pong, J. C. An Estelle-based incremental protocol design system. Journal of Systems and Software, 36, 2(1997),115–135.
  53. Jan, P. , Grzymala-Busse, J. W. and Zdzislaw, S. H. Melanoma prediction usingData mining system LERS. In Proceedings of the 25th Annual International Computer Software and Applications Conference (COMPSAC'01), (Chicago, IL, USA,2001), IEEE Computer Society,615-620.
  54. Jaworski W. Rule Induction: Combining Rough Set and Statistical Approaches. in Proceedings of the Sixth International Conference on Rough Sets and Current Trends in Computing (RSCTC'2008),Ohio, USA, Lecture Notes in Artificial Intelligence- 5306, (Ohio, USA,2008),Springer-Verlag,170-180.
  55. Jensen, R. and Shen,Q. A Rough Set – Aided system for Sorting WWW Book-marks. In N. Zhong et al. (Eds. ), Web Intelligence: Research and Development, Springer,Maebashi , Japan,2001,95-105.
  56. Kira, K. and Rendell,L. A. The Feature selection Problem: Traditional Methods and a New Algorithm. in Proceedings of Association for the Advancement of Artificial Intelligence( AAAI ),(SanJose, CA,USA,1992), MIT Press,129-134.
  57. Kohavi, R. and Frasca, B. Useful feature subsets and Rough set Reducts. In Proceedings of the Third International Workshop on Rough Sets and Soft Computing,(, San Jose, CA,1994), San Jose State University ,310-317.
  58. Komorowski ,J. Pawlak ,Z. ,Polkowski ,L. and Skowron, A. A Rough set perspective on Data and Knowledge. In Klosgen, W. and Zytkow, J. (Eds). The Handbook of Data mining and Knowledge discovery, (Oxford, 1999),Oxford University Press,134-149.
  59. Kryszkiewicz, M. Rough Set Approach to Incomplete Information Systems. Journal of Information Sciences. 112(1998), 39–49.
  60. Kusiak, A. Feature transformation methods in data mining. IEEE Transaction on Electronics Packaging Manufacturing, 24,3(2001),214–221.
  61. Kusiak, A. Rough set theory: A Data Mining tool for semiconductor manufacturing. IEEE Transactions on Electronics Packaging Manufacturing , 24 ,1(2001) , 44-50 .
  62. Law, R. and Au, N. Relationship modeling in tourism shopping: A decision rules induction approach. Tourism Management, 21(2000), 241–249.
  63. Li, D. , and Zhang, W. Gene selection using rough set theory. in Proceedings of 1st International Conference on Rough Sets and Knowledge Technology(RSKT 2006), (Chongqing, China,2006), Springer-Verlag,778-785.
  64. Li, J. ,Pattaraintakorn,P. and Cercone, N . Rule Evaluations, Attributes, and Rough Sets: Extension and a Case Study. In Peters, J. F. ; Düntsch, I. ; Grzymala-Busse, J. ; Orlowska, E. ; Polkowski, L. (Eds. ) Transactions on Rough Sets VI: Commemorating Life and Work of Zdislaw Pawlak, Part I, LNCS 4374, Springer-Verlag ,Berlin, Heidelberg ,2007,152-171.
  65. Liang, J. Y. and Xu, Z. B. The algorithm on knowledge reduction in incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems ,24(2002), 95–103.
  66. Lingras, P. and Yao, Y. Data mining using extensions of rough set model. Journal of American Society for Information Science , 49,5(1998),415–422 .
  67. Lingras P. Unsupervised rough set classification using Gas. Journal of Intelligent Information System, 16(2001), 215–228.
  68. Lingras, P. , Hogo, M. and Snorek, M. Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets. Web Intelligence and Agent System: An International Journal, 2,3(2004),217–230.
  69. Lingras, P. , Hogo, M. , Snorek, M. and West, C. Temporal analysis of clusters of supermarket customers: conventional versus interval set approach. Information Sciences , 172(2005), 215–240.
  70. Liu,H. and Setiono,R. Feature selection and classification – a probabilistic wrapper approach. In Proceedings of the 9th International Conference on Industrial and Engineering Applications of AI and ES, (Fukuoka, Japan,1996), Gordon and Breach Science Publishers,419-424 .
  71. Liu,H. and Setiono,R. Neural Network feature selector. IEEE Transactions on Neural Networks, 8 ,3(1997),645-662.
  72. Mazlack, L. J. , He, A. , Zhu, Y. and Coppock, S. A rough set approach in choosing partitioning attributes. In Proceedings of the ISCA 13th, International Conference of Computer Applications in Industry and Engineering( CAINE-2000), (Hawaii, USA,2000),ISCA,1-6.
  73. Mitra, S. An evolutionary rough partitive clustering. Pattern Recognition Letters, 25(2004), 1439–1449.
  74. Nguyen, H. S. On the decision table with maximal number of reducts. Electronic Notes in Theoretical Computer Science, 82,4(2003),pp. 1–8.
  75. Pal, S. K. , Polkowski, L. and Skowron, A. (eds. ) . Rough--Neural Computing- Techniques for Computing with Words. Springer-Verlag ,Berlin,2004.
  76. Parmar, D. , Wu, T. and Blackhurst, J. MMR: An algorithm for clustering categorical data using rough set theory. Data and Knowledge Engineering, 63(2007), 879–893.
  77. Pawlak, Z. Rough sets. International Journal of Computer and Information Sciences, 11(1982), 341-356.
  78. Pawlak, Z. Rough sets and fuzzy sets. Fuzzy Sets and Systems, 17(1985),99-102.
  79. Pawlak ,Z. Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Boston, MA,1991.
  80. Pawlak, Z. and Skowron,A. Rough membership functions, In R. R Yaeger, M. Fedrizzi and J. Kacprzyk (eds. ). Advances in the Dempster Shafer Theory of Evidence(pp. 251-271), John Wiley & Sons, Inc, Toronto, Singapore,1994,251-271.
  81. Pawlak, Z. Hard and soft sets . In W. P. Ziarko(Ed. ), Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag ,London,1994,130-135.
  82. Pawlak, Z. , Grzymala-Busse, J. W. , Slowinski, R. and Ziarko, W. Rough Sets. Communications of ACM , 38,11(1995),89–95.
  83. Pawlak, Z. and Skowron, A. Rudiments of rough sets. Information Sciences, 177 ,1(2007),3–27.
  84. Peters, G. and Lampart, M. A partitive rough clustering algorithm. Proceedings of the Fifth International Conference on Rough Sets and Current Trends in Computing (RSCTC'06), Lecture Notes in Artificial Intelligence, LNAI-4259, (Kobe, Japan,2006), Springer,657-666.
  85. Peters, G. Rough clustering and regression analysis. in Proceedings of 2007 IEEE Conference on Rough Sets and Knowledge Technology (RSKT'07), Lecture Notes in Artificial Intelligence, LNAI-4481, pp. 292–299 (Toronto, Canada,2007), John Wiley & Sons Inc,292-299.
  86. Peters, G. and Weber, R. A dynamic approach to rough clustering. in Proceedings of the Seventh International Conference on Rough Sets and Current Trends in Computing (RSCTC'08), Lecture Notes in Artificial Intelligence, LNAI-5306, (USA,2008), Springer,379-388.
  87. Peters, G. , Lampart, M. and Weber, R. Evolutionary rough k-medoid clustering. Transactions on Rough Sets VIII, Lecture Notes in Computer Science, 5084(2008),289–306.
  88. Polkowski, L. and Skowron, A. Rough mereology: a new paradigm for approximate reasoning. International Journal of Approximate Reasoning, 15(1996), 333–365.
  89. Polkowski, L. and Skowron, A. (eds. ). Rough Sets in Knowledge Discovery I & II'. , Physica-Verlag, Heidelberg,1998.
  90. Polkowski, L. , Tsumoto, S. and Lin, T. Y. (eds. ). Rough Set Methods and Applications. New Developments in Knowledge Discovery in Information Systems, Physica-Verlag ,Heidelberg,2000.
  91. Predki,B. , Slowinski, R. ,Stefanowski, J. ,Susmaga,R. and Wilk,Sz. ROSE. Software implementation of the rough set theory. Lecture Notes in Computer Science, 1424(1998),605–608.
  92. Quandt, R. The estimation of the parameters of a linear regression system obeying two separate regimes. Journal of American Statistics Assocociation, 53(1958), 873–880.
  93. Raman,B. and Loerger,T. R. Instance-based filter for feature selection. Journal of Machine Learning Research, l(2002),1-23.
  94. Ratnoo,S. and Bharadwaj, K. K. A Parallel Genetic Algorithm approach to automated discovery of censored production rules. in Proceedings of IASTED International Conference on Artificial Intelligence and Applications (AIA07),(Innsbruck, Austria,2007), ACTA Press, 435–441.
  95. Ratnoo,S. and Bharadwaj, K. K. Distributed mining of censored production rules in data streams. In Proceedings of the 7th WSEAS International Conference on Artificial intelligence, Knowledge engineering and Databases (AIKED08), (Cambridge, UK,2008), University of Cambridge, 500– 505.
  96. Ratnoo,S. and Bharadwaj, K. K. A Parallel genetic programming approach for automated discovery of censored production rules with fuzzy hierarchy. In Proceedings of International Conference on Information Processing, (Bangalore, India,2008), Springer,624-633.
  97. Ratnoo,S. and Bharadwaj,K. K. Discovery of Exceptions: A Step towards Perfection,NSS In Y. Xiang, J. Lopez, H. Wang and W. Zhou (Eds. ),Third International Conference on Network and System Security,(Queensland, Australia,2009), IEEE Computer Society,540-545.
  98. Ratnoo,S and Bharadwaj,K. K. Combining Rough Set and Evolutionary Approach for Automated Discovery of Censored Production Rules with Fuzzy Hierarchy. Journal of Intelligent Systems ,19,1(2010), 47-78.
  99. Richards, D. and Compton, P. An alternative verification and validation technique for an alternative knowledge representation and acquisition technique. Knowledge-Based Systems, 12,1–2(1999), 55–73.
  100. Salamo, M. and Golobardes, E. Analysing rough sets weighting methods for case-based reasoning systems. Journal of Artificial Intelligence,1,15(2002), 34–43.
  101. Salamo, M. and Golobardes, E. Deleting and building sort out techniques for case base maintenance, In Craw, S. , Preece, A. (Eds. ). Proceedings of the. 6th European Conference on Case-Based Reasoning, (Scotland, UK,2002), Springer,365-379.
  102. Salamo, M. and Golobardes, E. Unifying weighting and case reduction methods based on rough sets to improve retrieval. in Proceedings of the 5th International. Conference on Case-Based Reasoning, (Trondheim, Norway ,2003), Springer,494-508.
  103. Salamo, M. and Golobardes, E. Global, local and mixed case base maintenance techniques. In Proceedings of the 6th Congres Catala d'Intel_ligencia Artificial, (Amsterdam, Netherlands,2004) , IOS Press ,127–134.
  104. Salton, G. The SMART retrieval system. Prentice-Hall, Englewood Cliffs ,1971.
  105. Shan, N. and Ziarko, W. An incremental learning algorithm for constructing decision rules. In R. S. Kluwer (Ed. ). Rough sets, fuzzy sets and knowledge discovery, (Berlin, 1994), Springer-Verlag,326-334.
  106. Shen, Q. and Chouchoulas, A. A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Engineering Applications of Artificial Intelligence, 13,3(2000),263–278.
  107. Shiotani, S. , Fukuda, T. and Shibata, T. A neural network architecture for incremental learning. Neurocomputing, 9(1995), 111–130.
  108. Shyng, J. -Y. , Wang, F. -K. , Tzeng, G. -H. and Wu, K. -S. Rough set theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications, 32,1(2007),56–64.
  109. Skowron, A. and Stepaniuk, J. Intelligent systems based on rough set approach. in Proceedings of the International Workshop Rough Sets: State of the Art and Perspective,Extended Abstracts, (Kiekrz, Poland,1992),IDSR,62-64.
  110. Slowinski, R. (ed. ). Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Boston,1992.
  111. Slowinski, R. and Vanderpooten, D. Similarity relations as a basis for rough approximations. In P. P. Wang, (Eds), Advances in Machine Intelligence and Soft-Computing, (NC, USA,1997), Duke University Press,17–33.
  112. Stefanowski, J. On rough set based approaches to induction of decision rules. In A. Skowron & L. Polkowski (Eds. ). Rough sets in knowledge discovery , Physica Verlag ,Heidelberg ,1998,500-529.
  113. Swiniarski, R. and Nguyen, J. Rough set expert system for texture classification based on 2D spectral features. In Proceedings of the Third Biennial European Joint Conference on Engineering Systems Design and Analysis ESDA_96 , (Montpellier, France,1996), ESDA ,3-8.
  114. Traina Jr,C. , Traina,A. , Wu,L. and Faloutsos,C. Fast Feature selection using the fractal dimension. in Proceedings of the 15th Brazilian Symposium on Databases(SBBD), (Maringa, Brazil,2000), Brazilian Computer Society Press,177-188.
  115. Tsumoto, S. Extraction of Experts' Decision Rules from Clinical Databases using Rough Set Model. Journal of Intelligent Data Analysis, 2,3(1998),215-227.
  116. Tsumoto, S. Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Systems with Applications, 24,2(2003),189–197.
  117. Upadhyaya, S. , Arora A. and Jain ,R. Rough Set Theory: Approach for Similarity Measure in Cluster Analysis. in Proceedings of 2006 International Conference on Data Mining, (Hong Kong, China,2006), IEEE Computer Society , 353-356.
  118. Voges, K. E, Pope, N. K and Brown, M. R. Cluster analysis of marketing data examining on-line shopping orientation: a comparison of k-means and rough clustering approaches', In: Abbass HA, Sarker RA, Newton CS,(Eds. ) Heuristics and Optimization for Knowledge Discovery, Idea Group publishing, Hershey, PA,2002,208-225.
  119. Voges, K. E, Pope, N. K and Brown, M. R. A rough cluster analysis of shopping orientation data. in Proceedings of Australian and New Zealand Marketing Academy Conference, (Melbourne,2003), Adelaide,1625-1631.
  120. Wang, Q. H. and Li, J. R. A rough set-based fault ranking prototype system for fault diagnosis. Engineering Applications of Artificial Intelligence, 17,8(2004),909–917.
  121. Wang, X. , Yang, J. , Jensen, R. and Liu, X. Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Computer Methods and Programs in Biomedicine, 83(2006), 147–156.
  122. Wroblewski, J . Finding minimal reducts using genetic algorithms. In Wang, P. P. (Ed), Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences, (North Carolina, USA,1995), Rough sets & Computer Science,186-189.
  123. Wroblewski, J. Theoretical foundations of order-based genetic algorithms, Fundamenta Informaticae, IOS Press,28(1996) , 423-430.
  124. Wroblewski, J. Genetic algorithms in decomposition and classification problems. In Polkowski, L. & Skowron, A. (Eds. ), Rough sets in Knowledge Discovery 1: Applications, Case Studies and Software System, Physica-Verlag ,Heidelberg ,1998,472-492.
  125. Yao, Y. Y. On combining rough and fuzzy sets. in Lin, T. Y. (Ed. ). Proceedings of the Computer Science Conference CSC'95 Workshop on Rough Sets and Database Mining, (Nashville, Tennessee, USA, 1995), San Jose State University,165-172.
  126. Yao, Y. Y. , Li, X. , Lin, T. Y. and Liu, Q. Representation and Classification of Rough Set Models. Soft Computing: Rough Sets, Fuzzy Logic, Neural Network, Uncertainty Management and knowledge Discovery, Society for Computer Simulation ,CA, USA ,1995,44-47.
  127. Yao, Y. Y. Two views of the theory of rough sets in finite universes. Approximate Reasoning, 15,4(1996), 191–317.
  128. Yao, Y. Y, Lingras, P. , Wang, R and Miao, D. Interval set cluster analysis: a re-formulation. in Proceedings of the International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing (RSFDGrC'09), Lecture Notes in Computer Science, LNCS-5908, (Berlin, Heidelberg,2009),Springer-Verlag,398-405.
  129. Yao, Y. Y. Three-way decisions with probabilistic rough sets. . Information Sciences, 180,3(2010),341-353.
  130. Ye, N. and Li, X. A scalable, incremental learning algorithm for classification problems. Computers and Industrial Engineering, 43(2002), 677–692.
  131. Zadeh, L. A. A new direction in AI: Toward a computational theory of perceptions. AI Magazine, 22,1(2001),73-84.
  132. Zaki, M. J. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12,3(2000),372–390.
  133. Zhang, M. and Yao, J. T. A rough set approach to feature selection. Proceedings of the 23rd International Conference of the North American Fuzzy Information Processing Society (NAFIPS 2004), (Banff, Canada,2004), North America Fuzzy Information Processing Society,434-439.
  134. Zhong, N. , Dong, J. Z. and Ohsuga, S. Using Rough sets with heuristics for feature selection. Journal of Intelligent Information Systems, 16(2001), 199-214.
  135. Zhu, X. Semi-Supervised Learning with Graphs. PhD thesis, Carnegie Mellon University,USA,2005.
  136. Ziarko, W. P. (Ed. ) . Rough Sets, Fuzzy Sets and Knowledge Discovery. in Proceedings of International Workshop on Rough Set and Knowledge Discovery,. Canada, Springer-Verlag, 1994.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering Rule Induction Feature Selection

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