Abstract
A rough set approach to mining incomplete data is presented in this paper. Our main tool is an attribute-value pair block. A characteristic set, a generalization of the elementary set well-known in rough set theory, may be computed using such blocks. For incomplete data sets three different types of global approximations: singleton, subset and concept are defined. Additionally, for incomplete data sets a local approximation is defined as well.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Cyran, K.A.: Modified indiscernibility relation in the theory of rough sets with real-valued attributes: Application to recognition of fraunhofer diffraction patterns. Transactions on Rough Sets 9, 14–34 (2008)
Dai, J., Xu, Q., Wang, W.: A comparative study on strategies of rule induction for incomplete data based on rough set approach. International Journal of Advancements in Computing Technology 3, 176–183 (2011)
Dardzinska, A., Ras, Z.W.: Chasing unknown values in incomplete information systems. In: Workshop Notes, Foundations and New Directions of Data Mining, in Conjunction with the 3-rd International Conference on Data Mining, pp. 24–30 (2003)
Dardzinska, A., Ras, Z.W.: On rule discovery from incomplete information systems. In: Workshop Notes, Foundations and New Directions of Data Mining, in conjunction with the 3-rd International Conference on Data Mining, pp. 24–30 (2003)
Greco, S., Matarazzo, B., Slowinski, R.: Dealing with missing data in rough set analysis of multi-attribute and multi-criteria decision problems. In: Zanakis, H., Doukidis, G., Zopounidised, Z. (eds.) Decision Making: Recent Developments and Worldwide Applications, pp. 295–316. Kluwer Academic Publishers, Dordrecht (2000)
Grzymala-Busse, J.W.: On the unknown attribute values in learning from examples. In: Proceedings of the ISMIS-1991, 6th International Symposium on Methodologies for Intelligent Systems, pp. 368–377 (1991)
Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Workshop Notes, Foundations and New Directions of Data Mining, in Conjunction with the 3-rd International Conference on Data Mining, pp. 56–63 (2003)
Grzymala-Busse, J.W.: Data with missing attribute values: Generalization of indiscernibility relation and rule induction. Transactions on Rough Sets 1, 78–95 (2004)
Grzymała-Busse, J.W.: Characteristic relations for incomplete data: A generalization of the indiscernibility relation. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. Current Trends, vol. 3066, pp. 244–253. Springer, Heidelberg (2004)
Grzymala-Busse, J.W.: Three approaches to missing attribute values—a rough set perspective. In: Proceedings of the Workshop on Foundation of Data Mining, in Conjunction with the Fourth IEEE International Conference on Data Mining, pp. 55–62 (2004)
Grzymała-Busse, J.W.: Incomplete data and generalization of indiscernibility relation, definability, and approximations. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 244–253. Springer, Heidelberg (2005)
Grzymala-Busse, J.W.: A comparison of traditional and rough set approaches to missing attribute values in data mining. In: Proceedings of the 10-th International Conference on Data Mining, Detection, Protection and Security, Royal Mare Village, Crete, pp. 155–163 (2009)
Grzymala-Busse, J.W.: Mining data with missing attribute values: A comparison of probabilistic and rough set approaches. In: Proceedings of the 4-th International Conference on Intelligent Systems and Knowledge Engineering, pp. 153–158 (2009)
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Handling missing attribute values. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 37–57. Springer-Verlag, Heidelberg (2005)
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: An experimental comparison of three rough set approaches to missing attribute values. Transactions on Rough Sets 6, 31–50 (2007)
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Improving quality of rule sets by increasing incompleteness of data sets. In: Cordeiro, J., Shishkov, B., Ranchordas, A., Helfert, M. (eds.) ICSOFT 2008. Communications in Computer and Information Science, vol. 47, pp. 241–248. Springer, Heidelberg (2009)
Grzymala-Busse, J.W., Grzymala-Busse, W.J., Goodwin, L.K.: A comparison of three closest fit approaches to missing attribute values in preterm birth data. International Journal of Intelligent Systems 17(2), 125–134 (2002)
Grzymala-Busse, J.W., Grzymala-Busse, W.J., Hippe, Z.S., Rzasa, W.: An improved comparison of three rough set approaches to missing attribute values. In: Proceedings of the 16-th Int. Conference on Intelligent Information Systems, pp. 141–150 (2008)
Grzymała-Busse, J.W., Hu, M.: A comparison of several approaches to missing attribute values in data mining. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 378–385. Springer, Heidelberg (2001)
Grzymala-Busse, J.W., Rzasa, W.: Local and global approximations for incomplete data. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 244–253. Springer, Heidelberg (2006)
Grzymala-Busse, J.W., Rzasa, W.: Local and global approximations for incomplete data. Transactions on Rough Sets 8, 21–34 (2008)
Grzymala-Busse, J.W., Wang, A.Y.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values. In: Proceedings of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC 1997) at the Third Joint Conference on Information Sciences (JCIS 1997), pp. 69–72 (1997)
Hong, T.P., Tseng, L.H., Chien, B.C.: Learning coverage rules from incomplete data based on rough sets. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3226–3231 (2004)
Kryszkiewicz, M.: Rough set approach to incomplete information systems. In: Proceedings of the Second Annual Joint Conference on Information Sciences, pp. 194–197 (1995)
Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113(3-4), 271–292 (1999)
Latkowski, R.: On decomposition for incomplete data. Fundamenta Informaticae 54, 1–16 (2003)
Latkowski, R., Mikołajczyk, M.: Data decomposition and decision rule joining for classification of data with missing values. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 254–263. Springer, Heidelberg (2004)
Li, H., Yao, Y., Zhou, X., Huang, B.: Two-phase rule induction from incomplete data. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 47–54. Springer, Heidelberg (2008)
Li, D., Deogun, I., Spaulding, W., Shuart, B.: Dealing with missing data: Algorithms based on fuzzy set and rough set theories. Transactions on Rough Sets 4, 37–57 (2005)
Peng, H., Zhu, S.: Handling of incomplete data sets using ICA and SOM in data mining. Neural Computing and Applications 16, 167–172 (2007)
Li, T., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowledge-Based Systems 20(5), 485–494 (2007)
Nakata, M., Sakai, H.: Rough sets handling missing values probabilistically interpreted. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 325–334. Springer, Heidelberg (2005)
Qi, Y.S., Sun, H., Yang, X.B., Song, Y., Sun, Q.: Approach to approximate distribution reduct in incomplete ordered decision system. Journal of Information and Computing Science 3, 189–198 (2008)
Qi, Y.S., Wei, L., Sun, H.J., Song, Y.Q., Sun, Q.S.: Characteristic relations in generalized incomplete information systems. In: International Workshop on Knowledge Discovery and Data Mining, pp. 519–523 (2008)
Song, J., Li, T., Ruan, D.: A new decision tree construction using the cloud transform and rough sets. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 524–531. Springer, Heidelberg (2008)
Stefanowski, J., Tsoukiàs, A.: On the extension of rough sets under incomplete information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 73–82. Springer, Heidelberg (1999)
Stefanowski, J., Tsoukias, A.: Incomplete information tables and rough classification. Computational Intelligence 17(3), 545–566 (2001)
Wang, G.: Extension of rough set under incomplete information systems. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1098–1103 (2002)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough Sets. In: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z., Grzymala-Busse, J.W., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 38, 89–95 (1995)
Grzymala-Busse, J.W., Rzasa, W.: A local version of the MLEM2 algorithm for rule induction. Fundamenta Informaticae 100, 99–116 (2010)
Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 243–250 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grzymała-Busse, J.W. (2011). Mining Incomplete Data—A Rough Set Approach. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-24425-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
eBook Packages: Computer ScienceComputer Science (R0)