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Information Mining for Big Information

  • Yuichi GotoEmail author
Chapter
Part of the Studies in Big Data book series (SBD, volume 8)

Abstract

Knowledge discovery of database or data (KDD) is to acquire knowledge from data. Mining interesting patterns from data or information granules is the most important process of KDD. The post-process of the mining that is to acquire knowledge from the interesting patterns is also important. As obtained interesting patterns increases, it becomes hard for analysts to do the post-process of the mining because they have done the process empirically and manually. This chapter has investigated the post-process of the mining, and presented a support method and tools for the process. Information mining is a process to acquire knowledge from the interesting patterns discovered by mining from data or information granules. Consistent verification, information abstraction, hypothesis generation, hypothesis verification, and information deduction are activities of information mining. Current data mining methods and information granulation methods are suitable for information abstract, but not suitable for the other activities. The present author has shown that strong relevant logic-based reasoning is a systematic method for supporting information mining, and introduced a forward reasoning engine, a truth maintenance system, and epistemic programming can be used for support tools of the information mining with strong relevant logic-based reasoning.

Keywords

Information mining Strong relevant logics Forward reasoning engine Truth maintenance system Epistemic programming 

References

  1. 1.
    Anderson, A.R., Belnap Jr, N.D.: Entailment: the Logic of Relevance and Necessity, vol. 1. Princeton University Press, Princeton (1975)zbMATHGoogle Scholar
  2. 2.
    Anderson, A.R., Belnap Jr, N.D., Dunn, J.M.: Entailment: the Logic of Relevance and Necessity, vol. 2. Princeton University Press, Princeton (1992)zbMATHGoogle Scholar
  3. 3.
    Cheng, J.: A strong relevant logic model of epistemic processes in scientific discovery. In: Kawaguchi, E., Kangassalo, et al. (eds.) Information Modeling and Knowledge Bases XI. Frontiers in Artificial Intelligence and Applications, vol. 61, pp. 136–159. IOS Press, Amsterdam (2000)Google Scholar
  4. 4.
    Cheng, J.: Strong relevant logic as the universal basis of various applied logics for knowledge representation and reasoning. In: Kiyoki, Y., et al. (eds.) Information Modeling and Knowledge Bases XVII. Frontiers in Artificial Intelligence and Applications, vol. 136, pp. 310–320. IOS Press, Amsterdam (2006)Google Scholar
  5. 5.
    Cheng, J.: A temporal relevant logic approach to modeling and reasoning about epistemic processes. In: 2009 fifth international conference on semantics, knowledge and grids (SKG 2009), pp. 19–25. IEEE Computer Society, Beijing (2009)Google Scholar
  6. 6.
    Cheng, J., Nara, S., Goto, Y.: FreeEnCal: a forward reasoning engine with general-purpose. In: Apolloni, B., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, 12–14, Sept 2007. Proceedings, Part II. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 4693, pp. 444–452. Springer, Berlin (2007)Google Scholar
  7. 7.
    Davenport, T.H., Patil, D.J.: Data scientist: the sexiest job of the 21st century. Harvard Bus. Rev. 90(10), 70–77 (2012)Google Scholar
  8. 8.
    Davis, M.: The early history of automated deduction. In: Robinson, A., Voronkov, A. (eds.) Handbook of Automated Reasoning, pp. 5–15. Elsevier and MIT Press, Amsterdam/Cambridge (2001)Google Scholar
  9. 9.
    de Kleer, J.: An assumption-based TMS. Artif. Intell. 28(2), 127–162 (1986)CrossRefGoogle Scholar
  10. 10.
    Doyle, J.: A truth maintenance system. Artif. Intell. 12(3), 231–272 (1979)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Dubois, D., Berre, D.L., Prade, H., Sabbadin, R.: Using possibilistic logic for modeling qualitative decision: ATMS-based algorithms. Fundamenta Informaticae 37(1–2), 1–30 (1999)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Dubois, D., Lang, J., Prade, H.: Handling uncertain knowledge in an ATMS using possibilistic logic. In: Ras, Z., Emrich, M. (eds.) Methodologies for Intelligent Systems, 5: Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems Held 25–27 Oct 1990, pp. 252–259. Elsevier, Amsterdam (1990)Google Scholar
  13. 13.
    Fan, W., Bifet, A.: Mining big data: current status, and forecast to the future. ACM SIGKDD Explor. Newslett. 14(2), 1–5 (2012)CrossRefGoogle Scholar
  14. 14.
    Fang, W., Takahashi, I., Goto, Y., Cheng, J.: Practical implementation of EPLAS: an epistemic programming language for all scientists. In: 2011 international conference on machine learning and cybernetics (ICMLC 2011), pp. 608–616. IEEE, Guilin (2011)Google Scholar
  15. 15.
    Fayyad, U., Piatetsky-shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–54 (1996)Google Scholar
  16. 16.
    García-Martínez, R., Britos, P., Rodríguez, D.: Information mining processes based on intelligent systems. In: Ali, M., Bosse, T., Hindriks, K., Hoogendoorn, M., Jonker, C., Treur, J. (eds.) Recent Trends in Applied Artificial Intelligence, 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Amsterdam, The Netherlands, 17–21 June 2013. Proceedings. Lecture Notes in Computer Science, vol. 7906, pp. 402–410. Springer, Berlin (2013)Google Scholar
  17. 17.
    Goto, Y., Cheng, J.: A truth maintenance system for epistemic programming environment. In: 2012 eighth international conference on semantics, knowledge and grids (SKG 2012), pp. 1–8. IEEE Computer Society, Beijing (2012)Google Scholar
  18. 18.
    Goto, Y., Koh, T., Cheng, J.: A general forward reasoning algorithm for various logic systems with different formalizations. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based Intelligent Information and Engineering Systems, 12th International Conference, KES 2008, Zagreb, Croatia. 3–5 Sept 2008. Proceedings, Part II. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 5178, pp. 526–535. Springer, Berlin (2008)Google Scholar
  19. 19.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers, Burlington, MA (2011)Google Scholar
  20. 20.
    McAllester, D.A.: An Outlook on Truth Maintenance. AI Memos 551 (1980)Google Scholar
  21. 21.
    McDermott, D.: A general framework for reason maintenance. Artif. Intell. 50(3), 289–329 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Mitra, S., Pal, S.K., Mitra, P.: Data mining in soft computing framework: a survey. IEEE Trans. Neural Netw. 13(1), 3–14 (2002)CrossRefGoogle Scholar
  23. 23.
    Monai, F.F., Chehire, T.: Possibilistic assumption based truth maintenance system, validation in a data fusion application. In: The eighth international conference on uncertainty in artificial intelligence (UAI92), pp. 83–91. Morgan Kaufmann Publishers, Stanford, CA (1992)Google Scholar
  24. 24.
    O’Leary, D.E.: Artificial intelligence and big data. IEEE Intell. Syst. 28(2), 96–99 (2013)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Robinson, A., Voronkov, A. (eds.): Handbook of Automated Reasoning, vol. 1–2. Elsevier and MIT Press, Amsterdam/Cambridge (2001)zbMATHGoogle Scholar
  26. 26.
    Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33(2), 163–180 (2007)CrossRefGoogle Scholar
  27. 27.
    Pedrycz, W.: Granular Computing: Analysis and Design of Intelligent Systems. CRC Press/Taylor & Francis, Boca Roton (2013)CrossRefGoogle Scholar
  28. 28.
    Shen, Q., Zhao, R.: A credibilistic approach to assumption-based truth maintenance. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(1), 85–96 (2011)CrossRefGoogle Scholar
  29. 29.
    Skowron, A., Stepaniuk, J.: Information granules: towards foundations of granular computing. Int. J. Intell. Syst. 16(1), 57–85 (2001)CrossRefzbMATHGoogle Scholar
  30. 30.
    Stanojevic, M., Vranes, S., Velasevicngine, D.: Using truth maintenance systems: a tutorial. IEEE Intell. Syst. 9(6), 46–56 (1994)Google Scholar
  31. 31.
    Takahashi, I., Nara, S., Goto, Y., Cheng, J.: EPLAS: an epistemic programming language for all scientists. In: Shi, Y. (ed.) Computational Science—ICCS 2007: 7th International Conference, Beijing, China. 27–30 May 2007. Proceedings, Part I. Lecture Notes in Computer Science, vol. 4487, pp. 406–413. Springer, Berlin (2007)Google Scholar
  32. 32.
    Tkach, D.S.: Information mining with the IBM intelligent miner family. In: An IBM Software Solutions White Paper. pp. 1–29. IBM (1998)Google Scholar
  33. 33.
    Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)CrossRefGoogle Scholar
  34. 34.
    Zadeh, L.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4(2), 103–111 (1996)CrossRefMathSciNetGoogle Scholar
  35. 35.
    Zadeh, L.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90(2), 111–127 (1997)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information and Computer SciencesSaitama UniversitySaitamaJapan

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