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Data Mining Approach for Decision and Classification Systems Using Logic Synthesis Algorithms

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Advanced Methods and Applications in Computational Intelligence

Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 6))

Abstract

This chapter discusses analogies between decision system and logic circuit. For example, the problem of data redundancy in decision system is solved by minimizing the number of attributes and removing redundant decision rules which is analogous to the argument reduction method for logic circuits. Another issue associated with the field of data mining lies in the induction of decision rules which in result provide a basis for decision-making tasks. A similar algorithm in logic synthesis is called minimization of Boolean function. An issue of reduction of the capacity required to memorize a decision table is solved by disassembling this table to the subsystems in such a way that the original one can be recreated through hierarchical decision making. In logic synthesis it is called functional decomposition and is used for efficient technology mapping of logic circuits. Due to different interpretation and application these tasks seem totally different, however the analogies allow logic synthesis algorithms to be used in the field of data mining. Moreover, by applying specialized logic synthesis methods, these three issues, i.e. feature reduction, rule induction, and hierarchical decision making, can be successfully improved.

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References

  1. Abdullah, S., Golafshan, L., Nazri, M.Z.A.: Re-heat simulated annealing algorithm for rough set attribute reduction. International Journal of the Physical Sciences 6(8), 2083–2089 (2011), doi:10.5897/IJPS11.218

    Google Scholar 

  2. Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, vol. 56, pp. 49–88. Physica-Verlag, Heidelberg (2000), doi:10.1007/978-3-7908-1840-6_3

    Chapter  Google Scholar 

  3. Borowik, G.: Methods of logic synthesis and their application in data mining. In: 1st Australian Conference on the Applications of Systems Engineering, ACASE 2012, Sydney, Australia, pp. 56–57 (2012) (electronic document)

    Google Scholar 

  4. Borowik, G., Łuba, T., Zydek, D.: Features reduction using logic minimization techniques. International Journal of Electronics and Telecommunications 58(1), 71–76 (2012), doi:10.2478/v10177-012-0010-x

    Article  Google Scholar 

  5. Brayton, R.K., Hachtel, G.D., McMullen, C.T., Sangiovanni-Vincentelli, A.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer Academic Publishers (1984)

    Google Scholar 

  6. Brzozowski, J.A., Łuba, T.: Decomposition of boolean functions specified by cubes. Journal of Multi-Valued Logic & Soft Computing 9, 377–417 (2003)

    MATH  Google Scholar 

  7. Dasgupta, S., Papadimitriou, C.H., Vazirani, U.V.: Algorithms. McGraw-Hill (2008)

    Google Scholar 

  8. Dash, R., Dash, R., Mishra, D.: A hybridized rough-PCA approach of attribute reduction for high dimensional data set. European Journal of Scientific Research 44(1), 29–38 (2010)

    Google Scholar 

  9. Feixiang, Z., Yingjun, Z., Li, Z.: An efficient attribute reduction in decision information systems. In: International Conference on Computer Science and Software Engineering, Wuhan, Hubei, pp. 466–469 (2008), doi:10.1109/CSSE.2008.1090

    Google Scholar 

  10. Feng, Q., Miao, D., Cheng, Y.: Hierarchical decision rules mining. Expert Systems with Applications 37(3), 2081–2091 (March 2010), DOI: 10.1016/j.eswa.2009.06.065

    Article  Google Scholar 

  11. Grzenda, M.: Prediction-oriented dimensionality reduction of industrial data sets. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part I. LNCS, vol. 6703, pp. 232–241. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Grzymała-Busse, J.W.: LERS – a system to learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advanced of the Rough Sets Theory. Kluwer Academic Publishers (1992)

    Google Scholar 

  13. 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)

    Chapter  Google Scholar 

  14. Grzymała-Busse, J.W., Grzymała-Busse, W.J.: Inducing better rule sets by adding missing attribute values. In: Chan, C.-C., Grzymała-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 160–169. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Hedar, A.R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Journal of Soft Computing – A Fusion of Foundations, Methodologies and Applications 12(9), 909–918 (2008), doi:10.1007/s00500-007-0260-1

    MATH  Google Scholar 

  16. Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: Rough and fuzzy-rough based approaches. IEEE Transactions on Knowledge and Data Engineering 16, 1457–1471 (2004), doi:10.1109/TKDE.2004.96

    Article  Google Scholar 

  17. Jing, S., She, K.: Heterogeneous attribute reduction in noisy system based on a generalized neighborhood rough sets model. World Academy of Science, Engineering and Technology 75, 1067–1072 (2011)

    Google Scholar 

  18. Kalyani, P., Karnan, M.: A new implementation of attribute reduction using quick relative reduct algorithm. International Journal of Internet Computing 1(1), 99–102 (2011)

    Google Scholar 

  19. Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial (1999)

    Google Scholar 

  20. Kryszkiewicz, M., Cichoń, K.: Towards scalable algorithms for discovering rough set reducts. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.Z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 120–143. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  21. Kryszkiewicz, M., Lasek, P.: FUN: Fast discovery of minimal sets of attributes functionally determining a decision attribute. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 76–95. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Lewandowski, J., Rawski, M., Rybiński, H.: Application of parallel decomposition for creation of reduced feed-forward neural networks. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 564–573. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  23. Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds.): Data mining, rough sets and granular computing. Physica-Verlag GmbH, Heidelberg (2002)

    MATH  Google Scholar 

  24. Łuba, T., Borowik, G., Kraśniewski, A.: Synthesis of finite state machines for implementation with programmable structures. Electronics and Telecommunications Quarterly 55, 183–200 (2009)

    Google Scholar 

  25. Łuba, T., Lasocki, R.: On unknown attribute values in functional dependencies. In: Proceedings of The Third International Workshop on Rough Sets and Soft Computing, San Jose, pp. 490–497 (1994)

    Google Scholar 

  26. Łuba, T., Lasocki, R., Rybnik, J.: An implementation of decomposition algorithm and its application in information systems analysis and logic synthesis. In: Ziarko, W. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing Series, pp. 458–465. Springer, Heidelberg (1994)

    Google Scholar 

  27. Łuba, T., Rybnik, J.: Rough sets and some aspects in logic synthesis. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory. Kluwer Academic Publishers (1992)

    Google Scholar 

  28. Nguyen, D., Nguyen, X.: A new method to attribute reduction of decision systems with covering rough sets. Georgian Electronic Scientific Journal: Computer Science and Telecommunications 1(24), 24–31 (2010)

    Google Scholar 

  29. Papadimitriou, C.H.: Computational complexity. Academic Internet Publ. (2007)

    Google Scholar 

  30. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)

    Google Scholar 

  31. Pei, X., Wang, Y.: An approximate approach to attribute reduction. International Journal of Information Technology 12(4), 128–135 (2006)

    Google Scholar 

  32. Peters, J.F., Skowron, A., Chan, C.-C., Grzymała-Busse, J.W., Ziarko, W.P. (eds.): Transactions on Rough Sets XIII. LNCS, vol. 6499. Springer, Heidelberg (2011)

    MATH  Google Scholar 

  33. Rawski, M., Borowik, G., Łuba, T., Tomaszewicz, P., Falkowski, B.J.: Logic synthesis strategy for FPGAs with embedded memory blocks. Electrical Review 86(11a), 94–101 (2010)

    Google Scholar 

  34. Selvaraj, H., Sapiecha, P., Rawski, M., Łuba, T.: Functional decomposition – the value and implication for both neural networks and digital designing. International Journal of Computational Intelligence and Applications 6(1), 123–138 (2006), doi:10.1142/S1469026806001782

    Article  Google Scholar 

  35. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory. Kluwer Academic Publishers (1992)

    Google Scholar 

  36. Słowiński, R., Sharif, E.: Rough sets analysis of experience in surgical practice. In: Rough Sets: State of The Art and Perspectives, Poznań-Kiekrz (1992)

    Google Scholar 

  37. Wang, C., Ou, F.: An attribute reduction algorithm based on conditional entropy and frequency of attributes. In: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation, ICICTA 2008, vol. 1, pp. 752–756. IEEE Computer Society, Washington, DC (2008), doi:10.1109/ICICTA.2008.95

    Chapter  Google Scholar 

  38. Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356–3373 (2008), doi:10.1016/j.ins.2008.05.010

    Article  MathSciNet  MATH  Google Scholar 

  39. Zupan, B., Bohanec, M., Bratko, I., Cestnik, B.: A dataset decomposition approach to data mining and machine discovery. In: KDD, pp. 299–302 (1997)

    Google Scholar 

  40. Espresso – multi-valued PLA minimization, http://embedded.eecs.berkeley.edu/pubs/downloads/espresso

  41. ROSE2 – Rough Sets Data Explorer, http://idss.cs.put.poznan.pl/site/rose.html

  42. ROSETTA – A Rough Set Toolkit for Analysis of Data, http://www.lcb.uu.se/tools/rosetta/

  43. RSES – Rough Set Exploration System, http://logic.mimuw.edu.pl/~rses/

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Authors and Affiliations

  1. Institute of Telecommunications, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Grzegorz Borowik

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  1. Grzegorz Borowik
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Correspondence to Grzegorz Borowik .

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Editors and Affiliations

  1. Wroclaw University of Technology, Wybrzeze Wyspianskiego Street 27, Wroclaw, 50-370, Poland

    Ryszard Klempous

  2. Wroclaw University of Technology, Wybrzeze Wyspianskiego Street 27, Wroclaw, 50-370, Poland

    Jan Nikodem

  3. of Applied Sciences, Upper Austria University, Softwarepark 11, Hagenberg, 4232, Austria

    Witold Jacak

  4. , Bld 1 Level 22, University of Technology, Broadway, Ultimo/Syd, Sydney, 2007, New South Wales, Australia

    Zenon Chaczko

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Borowik, G. (2014). Data Mining Approach for Decision and Classification Systems Using Logic Synthesis Algorithms. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol 6. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01436-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-01436-4_1

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01435-7

  • Online ISBN: 978-3-319-01436-4

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