Abstract
Rough sets framework has two appealing aspects. First, it is a mathematical approach to deal with vague concepts. Second, rough set techniques can be used in data analysis to find patterns hidden in the data. The number of applications of rough sets to practical problems in different fields demonstrates the increasing interest in this framework and its applicability. This thesis proposes a language that caters for implicit definitions of rough sets obtained by combining different regions of other rough sets. In this way, concept approximations can be derived by taking into account domain knowledge. A declarative semantics for the language is also discussed. It is then shown that programs in the proposed language can be compiled to extended logic programs under the paraconsistent stable model semantics. The equivalence between the declarative semantics of the language and the declarative semantics of the compiled programs is proved. This transformation provides the computational basis for implementing our ideas. A query language for retrieving information about the concepts represented through the defined rough sets is also discussed. Several motivating applications are described. Finally, an extension of the proposed language with numerical measures is presented. This extension is motivated by the fact that numerical measures are an important aspect in data mining applications.
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References
Pawlak, Z.: Rough sets. International Journal of Information and Computer Science 11, 341–356 (1982)
Nguyen, H.S., Nguyen, T.T., Skowron, A., Synak, P.: Knowledge discovery by rough set methods. In: Callaos, N.C. (ed.) Proc. of the International Conference on Information Systems Analysis and Synthesis (ISAS 1996), pp. 526–533 (1996)
Komorowski, J., Øhrn, A.: Modelling prognostic power of cardiac tests using rough sets. Journal of Artificial Intelligence in Medicine 15, 167–191 (1999)
Lazareck, L., Ramanna, S.: Classification of swallowing sound signals: A rough set approach. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 679–684. Springer, Heidelberg (2004)
Tay, F.E.H., Shen, L.: Economic and finantial prediction using rough sets model. European Journal of Operational Research 141, 641–659 (2002)
Midelfart, H., Komorowski, J.: A rough set framework for learning in a directed acyclic graph. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 144–155. Springer, Heidelberg (2002)
Pagliani, P.: Rough sets theory and logic-algebraic structures. In: Orlowska, E. (ed.) Incomplete Information: Rough Sets Analysis, pp. 109–190. Physics (1997)
Yao, Y.Y., Lin, T.Y.: Generalizations of rough sets using modal logic. Journal of Intelligent Automation and Soft Computing 2, 103–120 (1996)
Midelfart, H., Komorowski, J.: A rough set approach to inductive logic programming. In: Ziarko, W.P., Yao, Y.Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, p. 190. Springer, Heidelberg (2001)
Wygralak, M.: Rough sets and fuzzy sets - some remarks on interrelations. Journal of Fuzzy Sets and Systems 29, 241–243 (1989)
Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Slowinski, R. (ed.) Handbook of Applications and Advances of the Rough Sets Theory, pp. 204–232. Kluwer Academic Publishers, Dordrecht (1992)
Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)
Ziarko, W.: Variable precision rough set model. Journal of Computer and Systems Science 46, 39–59 (1993)
Øhrn, A., Komorowski, J.: ROSETTA: A rough set toolkit for analysis of data. In: Proc. of Third International Joint Conference on Information Sciences, Fifth International Workshop on Rough Sets and Soft Computing (RSSC 1997), Durham, NC, USA, vol. 3, pp. 403–407 (1997)
Ziarko, W., Fei, X.: VPRSM approach to WEB searching. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 514–521. Springer, Heidelberg (2002)
Doherty, P., Łukaszewicz, W., Szałas, A.: CAKE: A Computer Aided Knowledge Engineering Technique. In: van Harmelen, F. (ed.) Proc. of the 15th European Conference on Artificial Intelligence (ECAI 2002), pp. 220–224. IOS Press, Amsterdam (2002)
Małuszyński, J., Vitória, A.: Defining rough sets by extended logic programs. In: On-Line Proc. of Paraconsistent Computational Logic Workshop, PCL 2002 (2002), http://floc02.diku.dk/PCL/ , http://arxiv.org/list/cs.lo/0207#cs.lo/0207089
Vitória, A., Małuszyński, J.: A logic programming framework for rough sets. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 205–212. Springer, Heidelberg (2002)
Vitória, A., Damásio, C.V., Małuszyński, J.: Query answering for rough knowledge bases. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 197–204. Springer, Heidelberg (2003)
Damásio, C.V., Pereira, L.M.: A survey of paraconsistent semantics for logic programs. In: Gabbay, D.M., Smets, P. (eds.) Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 2, pp. 241–320. Kluwer Academic Publishers, Dordrecht (1998)
Vitória, A., Damásio, C.V., Małuszyński, J.: From rough sets to rough knowledge bases. Fundamenta Informaticae 57, 215–246 (2003)
Vitória, A., Damásio, C.V., Małuszyński, J.: Toward rough knowledge bases with quantitative measures. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 153–158. Springer, Heidelberg (2004)
Andersson, R.: Rough Knowledge Base System (2004), Available at, http://www.ida.liu.se/rkbs
Pawlak, Z.: Rough sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization. A New Trend in Decision-Making, pp. 3–98. Springer, Heidelberg (1999)
Ziarko, W.: Rough set approaches for discovery of rules and attribute dependencies. In: Kloesgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 328–338. Oxford University Press, Oxford (2002)
Slowinski, R., Vanderpooten, D.: Similarity relations as a basis for rough set approximations. In: Wang, P.P. (ed.) Advances in Machine Intelligence and Soft Computing, vol. 4, pp. 17–33 (1997)
Skowron, A., Polkowski, L.: Synthesis of decison systems from data tables. In: Lin, T.Y., Cercone, N. (eds.) Rough Sets and Data Mining Analysis of Imprecise Data, pp. 259–300. Kluwer Academic Publishers, Dordrecht (1997)
Bazan, J.G.: Discovery of decision rules by matching new objects against data tables. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 521–528. Springer, Heidelberg (1998)
Stefanowski, J.: On rough set approaches to induction of decision rules. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, Methodology and Applications, pp. 501–529. Springer, Heidelberg (1998)
Bazan, J.G.: Dynamic reducts and statistical inference. In: Proc. of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMIU 1996), vol. 3, pp. 1147–1152 (1996)
Lloyd, J.W.: Foundations of Logic Programming. Springer, Heidelberg (1987)
Nilsson, U., Małuszynski, J.: Logic, Programming and Prolog, 2nd edn. John Wiley & Sons, Chichester (1995), http://www.ida.liu.se/~ulfni/lpp/copyright.html
Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)
Brachman, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. Elsevier, Amsterdam (2004)
Pearce, D.: Answer sets and constructive logic, II: Extended logic programs and related non-monotonic formalisms. In: Pereira, L.M., Nerode, A. (eds.) Logic Programming and Nonmonotonic Reasoning - Proc. of the 2nd International Workshop, pp. 457–475. MIT Press, Cambridge (1993)
Sakama, C., Inoue, K.: Paraconsistent Stable Semantics for Extended Disjunctive Programs. Journal of Logic and Computation 5, 265–285 (1995)
Gelfond, M., Lifschitz, V.: Logic programs with classical negation. In: Warren, S. (ed.) Proc. of the Seventh International Conference on Logic Programming, pp. 579–597. MIT Press, Cambridge (1990)
Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R.A., Bowen, K. (eds.) Proc. of the Fifth International Logic Programming Conference and Symposium, Seattle, USA, pp. 1070–1080. MIT Press, Cambridge (1988)
Inoue, K., Sakama, C.: Negation as failure in the head. Journal of Logic Programming 35, 39–78 (1998)
Alferes, J.J., Leite, J.A., Pereira, L.M., Przymusinska, H., Przymusiski, T.C.: Dynamic updates of non-monotonic knowledge bases. Journal of Logic Programming. 45, 43–70 (2000)
Niemelä, I., Simons, P.: Efficient implementation of the well-founded and stable model semantics. In: Maher, M. (ed.) Proc. of the Joint International Conference and Symposium on Logic Programming, Bonn, Germany, pp. 289–303. MIT Press, Cambridge (1996)
Simons, P.: Smodels system, Available at, http://www.tcs.hut.fi/Software/smodels/
Eiter, T., Leone, N., Mateis, C., Pfeifer, G., Scarcello, F.: The KR system dlv: Progress report, comparisons and benchmarks. In: Cohn, A.G., Schubert, L., Shapiro, S.C. (eds.) KR 1998: Principles of Knowledge Representation and Reasoning, pp. 406–417. Morgan Kaufmann, San Francisco (1998)
The dlv project, Available at, http://www.dbai.tuwien.ac.at/proj/dlv/
Deransart, P., Ed-Bali, A., Cervoni, L.: Prolog: The Standard Reference Manual. Springer, Heidelberg (1996)
Belnap, N.D.: A useful four-valued logic. In: Epstein, G., Dunn, J.M. (eds.) Modern Uses of Multiple-Valued Logic, pp. 7–37. Reidel Publishing Company, Dordrecht (1977)
Belnap, N.D.: How computer should think. In: Rydle, G. (ed.) Contemporary Aspets of Philosophy, pp. 30–56. Oriel Press (1977)
Niemelä, I., Simons, P.: Smodels - an implementation of stable model and the well-founded semantics for normal logic programs. In: Fuhrbach, U., Dix, J., Nerode, A. (eds.) LPNMR 1997. LNCS(LNAI), vol. 1265, pp. 420–429. Springer, Heidelberg (1997)
Dantsin, E., Eiter, T., Gottlob, G., Voronkov, A.: Complexity and expressive power of logic programming. ACM Computing Surveys 33, 374–425 (2001)
Ziarko, W.: Acquisition of Hierarchy-Structured Probabilistic Decision Tables and Rules from Data. In: Proc. of the World Congress on Computational Intelligence, Honolulu (2002)
Reiter, R.: A logic for deafult reasoning. Journal of Artificial Intelligence 13, 81–132 (1980)
Andersson, R.: Implementation of a rough knowledge base system supporting quantitative measures. Master thesis, Linköping University, IDA (2004)
Zaniolo, C.: Key constraints and monotonic aggregates in deductive databases. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 109–134. Springer, Heidelberg (2002)
Faber, W., Leone, N., Pfeifer, G.: Recursive aggregates in disjunctive logic programs: Semantics and complexity. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 200–212. Springer, Heidelberg (2004)
Java. Sun Mycrosystems, Available at, http://java.sun.com/
XSB system, Available at, http://xsb.sourceforge.net/
Chen, W., Swift, T., Warren, D.S.: Efficient top-down computation of queries under the well-founded semantics. Journal of Logic Programming 24, 161–199 (1995)
Chen, W., Warren, D.S.: Tabled evaluation with delaying for general logic programs. Journal of the ACM 43, 20–74 (1996)
Sagonas, K., Swift, T.: An abstract machine for tabled execution of fixed-order stratified logic programs. Journal of the ACM TOPLAS 20, 586–635 (1998)
Swift, T.: Tabling for non-monotonic reasoning. Annals of Mathematics and Artifcial Intelligence 25, 201–240 (1999)
Doherty, P., Kachniarz, J., Szałas, A.: Using contextually closed queries for local closed-world reasoning in rough knowledge databases. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing, pp. 219–250. Springer, Heidelberg (2004)
Polkowski, L., Skowron, A.: Rough mereology. In: Proc. of the Eighth International Symposium on Methodologies for Intelligent Systems, pp. 85–94. Springer, Heidelberg (1994)
Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15, 333–365 (1997)
Polkowski, L., Skowron, A.: Rough mereological calculi of granules: A rough set approach to computation. Journal of Computational Intelligence 17, 472–492 (2001)
Midelfart, H., Lægreid, A., Komorowski, J.: Classification of gene expression data in an ontology. In: Crespo, J.L., Maojo, V., Martin, F. (eds.) ISMDA 2001. LNCS, vol. 2199, pp. 186–194. Springer, Heidelberg (2001)
Midelfart, H.: Knowledge Discovery from cDNA Microarrays and a priori Knowledge. PhD thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway (2003)
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Vitória, A. (2005). A Framework for Reasoning with Rough Sets. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets IV. Lecture Notes in Computer Science, vol 3700. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11574798_10
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