Reconsideration of the Effectiveness on Extracting Computer Diagnostic Rules by Automatically Defined Groups

  • Yoshiaki Kurosawa
  • Akira Hara
  • Kazuya Mera
  • Takumi Ichimura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4693)


Our aim is to manage computer systems without expert knowledge. We have proposed a method of diagnostic rule extraction from log files by using Automatically Defined Groups (ADG) based on Genetic Programming. However, this work less explained the effectiveness, especially, the characteristics of the acquired rules. Therefore, we re-evaluated the effectiveness by performing two experiments: the use of artificial log files and the use of real log files. As a result, we confirmed that ADG could acquire the rules composed of multiple terms. This characteristic is very important because we can judge the message that we must consider the co-occurrence of the words, i.e. ‘Error’ and ‘not’. Thus, we conclude that the ADG is effective for the diagnosis of the systems.


Genetic Programming Rule Extraction Data Mining 


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  1. 1.
    Andrews, J.H.: Theory and practice of log file analysis. Technical Report 524, Department of Computer Science, University of Western Ontario (1998)Google Scholar
  2. 2.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems 1(1), 5–32 (1999)CrossRefGoogle Scholar
  3. 3.
    Büchner, A.G., Baumgarten, M., Anand, S.S., Mulvenna, M.D., Hughes, J.G.: Navigation pattern discovery from internet data. In: Proc. of the Web Usage Analysis and User Profiling Workshop, pp. 25–30 (1999)Google Scholar
  4. 4.
    Kurosawa, Y., Hara, A., Ichimura, T., Kawano, Y.: Extraction of Error Detection Rules without Supervised Information from Log Files Using Automatically Defined Groups. In: Proc. of the IEEE International Conference on Systems, Man and Cybernetics (SMC2006), pp. 5314–5319 (2006)Google Scholar
  5. 5.
    Hara, A., Ichimura, T., Yoshida, K.: Discovering Multiple Diagnostic Rules from Coronary Heart Disease Database Using Automatically Defined Groups. International Journal of Manufacturing 16(6), 645–661 (2005)CrossRefGoogle Scholar
  6. 6.
    Hara, A., Ichimura, T., Takahama, T., Isomichi, Y.: Discovery of Cluster Structure and The Clustering Rules from Medical Database Using ADG; Automatically Defined Groups. In: Ichimura, T., Yoshida, K. (eds.) Knowledge-Based Intelligent Systems for Healthcare, pp. 51–86 (2004)Google Scholar
  7. 7.
    Hara, A., Nagao, T.: Construction and analysis of stock market model using ADG; Automatically Defined Groups. International Journal of Computational Intelligence and Applications (IJCIA) 2(4), 433–446 (2002)CrossRefGoogle Scholar
  8. 8.
    Lonvick, C.: The BSD Syslog Protocol, RFC3164 (August 2001)Google Scholar
  9. 9.
    Kurosawa, Y., Hara, A., Ichimura, T.: Preprocessing techniques for extracting computer diagnostic rules by ADG. In: SMCia2007. Proc. of the IEEE Three-Rivers Workshop on Soft Computing in Industrial Applications, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  10. 10.
    Kurosawa, Y., et al.: A description method of syntactic rules on filmscripts. Journal of Natural Language Processing (in Japanese) 12(6), 25–62 (2005)CrossRefGoogle Scholar
  11. 11.
    Mera, K., Kurosawa, Y., Ichimura, T.: Emotion Oriented Interaction system for Elderly People. In: Ichimura, T., Yoshida, K. (eds.) Knowledge Based Intelligent Systems for Health Care, Advanced Knowledge International (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yoshiaki Kurosawa
    • 1
  • Akira Hara
    • 1
  • Kazuya Mera
    • 1
  • Takumi Ichimura
    • 1
  1. 1.Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, HiroshimaJapan

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