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Business Application for Sales Transaction Data by Using Genome Analysis Technology

  • Naoki Katoh
  • Katsutoshi Yada
  • Yukinobu Hamuro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

We have recently developed an E-BONSAI (Extended BONSAI) for discovering useful knowledge from time-series purchase transaction data, developed by improving and adding new features to a machine learning algorithm for analyzing string pattern such amino acid sequence, BONSAI, proposed by Shimozono et al. in 1994. E-BONSAI we developed can create a good decision tree to classify positive and negative data for records whose attributes are either numerical, categorical or string patterns while other methods such as C5.0 and CART cannot deal with string patterns directly. We shall demonstrate advantages of E-BONSAI over existing methods for forecasting future demands by applying the methods to real business data. To demonstrate an advantage of E-BONSAI for business application, it is significant to evaluate it from the two perspectives. The first is the objective and technical perspective such as the prediction accuracy. The second is the management perspective such as the interpreterability to create new business action. Applying the E-BONSAI to forecast how long new products survive in instant noodle market in Japan, we have succeeded in attaining high prediction ability and discovering useful knowledge for domain experts.

Keywords

Prediction Accuracy Domain Expert Sales Volume Demand Forecast Character String 
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.

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References

  1. 1.
    Arikawa, S., Miyano, S., Shinohara, A., Kuhara, S., Mukouchi, Y., Shinohara, T.: A Machine Discovery from Amino Acid Sequences by Decision Trees over Regular Patterns. New Generation Computing 11, 361–375 (1993)zbMATHCrossRefGoogle Scholar
  2. 2.
    Bass, F.M.: A New Product Growth for Consumer Durables. Management Science 15, 215–227 (1969)CrossRefGoogle Scholar
  3. 3.
    Fourt, L.A., Woodlock, J.W.: Early Prediction of Market Success for New Grocery Products. Journal of Marketing 25(2), 30–38 (1960)CrossRefGoogle Scholar
  4. 4.
    Hamuro, Y., Kawata, H., Katoh, N., Yada, K.: A Machine Learning Algorithm for Analyzing String Patterns Helps to Discover Simple and Interpretable Business Rules from Purchase History. In: Progresses in Discovery Science, State-of-the-Art Surveys. LNCS, pp. 565–575 (2002)Google Scholar
  5. 5.
    Hirao, M., Hoshino, H., Shinohara, A., Takeda, M., Arikawa, S.: A practical algorithm to find the best subsequences patterns. Theoretical Computer Science 292(2), 465–479 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Kahn, E.B., McLister, L.: Grocery Revolution: The New Focus on the Consumer. Addison Wesley, Reading (1997)Google Scholar
  7. 7.
    Kalish, S.: A New Product Adoption Model with Pricing, Advertising and Uncertainty. Management Science 31, 1569–1585 (1985)zbMATHCrossRefGoogle Scholar
  8. 8.
    Kotler, P.: Marketing Management. Prentice Hall, Englewood Cliffs (2000)Google Scholar
  9. 9.
    Nakamura, H.: Marketing of New Products, Chuokeizai-sha (2001)Google Scholar
  10. 10.
    Nakanishi, M.: Advertising and Promotion Effects on Consumer Response to New Products. Journal of Marketing Research 10, 242–249 (1973)CrossRefGoogle Scholar
  11. 11.
    Parfitt, J.H., Collins, J.K.: Use of Consumer Panels for Brand Share Prediction. Journal of Marketing Research 5, 131–249 (1968)CrossRefGoogle Scholar
  12. 12.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)Google Scholar
  13. 13.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Francisco (1993)Google Scholar
  14. 14.
    Shimozono, S., Shinohara, A., Shinohara, T., Miyano, S., Kuhara, S., Arikawa, S.: Knowledge Acquisition from Amino Acid Sequences by Machine Learning System BONSAI. Trans. Information Processing Society of Japan 35, 2009–2018 (1994)Google Scholar
  15. 15.
    Toyota, H.: Introduction to Data Mining, Kodansha (2001)Google Scholar
  16. 16.
    Tsukimoto, H.: Practical Data Mining, Ohmsha (1999)Google Scholar
  17. 17.
    Yada, K.: The Future Direction of Active Mining in the Business World. Frontiers in Artificial Intelligence and Applications 79, 239–245 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Naoki Katoh
    • 1
  • Katsutoshi Yada
    • 2
  • Yukinobu Hamuro
    • 3
  1. 1.Department of Architecture and Architectural SystemsKyoto University KyotoKyotoJapan
  2. 2.Faculty of CommerceKansai UniversitySuita, OsakaJapan
  3. 3.Faculty of Business AdministrationOsaka Sangyo UniversityOsakaJapan

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