Skip to main content

Data Mining of Design Products and Processes

  • Chapter
Data Mining and Knowledge Discovery Handbook

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in massive database, in Proceedings of the ACM/SICMOD International Conference on Management of Data, p. 207–216, 1993.

    Google Scholar 

  • Arciszewski T., Mustafa M. and Ziarko W., A methodology of design knowledge acquisition for use in learning expert systems, International Journal of Man-Machine Studies, 27(1):23–32, 1987.

    Article  Google Scholar 

  • Bareiss R., Exemplar-Based Knowledge Acquisition, Academic press, Boston, MA, 1989.

    MATH  Google Scholar 

  • Braha D. (ed), Data Mining for Design and Manufacturing, Kluwer, 2001.

    Google Scholar 

  • Bobrow E. E. and Shafer D. W., Pioneering New Products. A Market Survival Guide, Dow Jones-Irwin, New York, 1987.

    Google Scholar 

  • Brachman R. J., Anand T., The process of knowledge discovery in databases, In Fayyad U. M., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R., editors, Advances in Knowledge Discovery and DM, pp 37–57, Menlo Park, CA, AAAI Press, 1996.

    Google Scholar 

  • Brodley C. E. and Smyth P., Applying classification algorithms in practice, Statistics and Computing, 7(1):45–56, 1997.

    Article  Google Scholar 

  • Browning T. C, Applying the Design Structure Matrix to system decomposition in integrated problems-A review and new directions, IEEE transaction on Engineering Management, 48(3):292–306, 2001

    Article  Google Scholar 

  • Carr M. J., Konda S. L., Monarch I. A., Ulrich F. C., and Walker C. F., Taxonomy-Based Risk Identification, SEI Technical Report SEI-93-TR-006, Software Engineering Institute, Pittsburgh, PA, 1993.

    Google Scholar 

  • Chapman P., Clinton J., Khabaza T, Reinartz T, and Wirth R., The CRISP-DM Process Model, Technical report, The CRIP-DM Consortium, 1999.

    Google Scholar 

  • Clark P. and Niblett T., The CN2 induction algorithm, Machine Learning, 3(4):261–283, 1989.

    Google Scholar 

  • Coulter N., Monarch I., and Konda S., Software engineering as seen through its research literature: A study in co-word analysis. Journal of the American Society for Information Science, 49(13):1206–1223, 1998.

    Article  Google Scholar 

  • Cooper R. G., Edgett S. J. and Kleinschmidt E. J., Portfolio Management for New Products, 2nd edition, Perseus Publishing, 2001.

    Google Scholar 

  • Dong A., & Agogino A. M., Text analysis for constructing design representations, Artificial Intelligence in Engineering, 11(2):65–75, 1997

    Article  Google Scholar 

  • Fisher D. H., Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139–172, 1987.

    Google Scholar 

  • Gebhardt E, Voß A., Gräther W., Schmidt-Belz B., Reasoning with Complex Cases, Kluwer Academic Publishers, 1997.

    Google Scholar 

  • Grecu D. L. and Brown D. C, Dimensions of machine learning in design, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12:117–121, 1998.

    Google Scholar 

  • Grigori D., Casati F, Castellanos M., Dayal U., Sayal M., and Shan M.-C., Business Process Intelligence, Computers in Industry, 53(3):321–343, 2004.

    Article  Google Scholar 

  • Herbst J. and Karagiannis D., Integrating machine learning and workflow management to support acquisition and adaptation of workflow models, International Journal of Intelligent Systems in Accounting, Finance and Management, 9:67–92, 2000.

    Article  Google Scholar 

  • Ivezic N. and Garrett J., A neural network-based machine learning approach for supporting synthesis, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 8(2): 143–161, 1994.

    Google Scholar 

  • Jiao J. and Zhang Y., Product portfolio identification based on association rule mining, Computer-Aided Design, 37(2): 149–172, 2005.

    Article  MathSciNet  Google Scholar 

  • Jiao J., Tseng M. M., Ma Q., and Zou Y., Generic bill of materials and operations for high-variety production management, Concurrent Engineering Research and Applications, 8(4):297–322, 2000.

    Article  Google Scholar 

  • Kibler D. and Langley P., Machine learning as an experimental science, Proceedings of the Third European Working Session on Learning, pp 81–92, Pittman, Glasgow, 1988.

    Google Scholar 

  • Kim P. and Ding Y. Optimal engineering design guided by data-mining methods, Technometrics, accepted, 2004.

    Google Scholar 

  • Krishnan V. and Ulrich K., Product development decisions: A Review of the Literature, Management Science, 47(1): 1–21, 2001.

    Article  Google Scholar 

  • Leary S. J., Bhaskar A., and Keane A. J., A derivative based surrogate model for approximating and optimizing the output of an expensive computer simulation, Journal of Global Optimization, 30: 39–58, 2004.

    Article  MathSciNet  MATH  Google Scholar 

  • Lu S. C.-Y. and Chen K., A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decisionmaking, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1(2):109–118, 1987.

    Google Scholar 

  • Mackenzie C. A., Inferring relational design grammars, Environment and Planning B: Planning and Design, 16(3):253–287, 1989.

    Google Scholar 

  • Madhusudan T., Zhao J. L., and Marshall B., A case-based reasoning framework for workflow model management. Data & Knowledge Engineering, 50(1):87–115, 2004.

    Article  Google Scholar 

  • Matthews P. C, Blessing L. T. M., and Wallace K. M., The introduction of a design heuristics extraction method. Advanced Engineering Informatics, 16(1):3–19, 2OO2.

    Article  Google Scholar 

  • McLaughlin S and Gero J. S., Acquiring expert knowledge from characterized designs, Artificial Intelligence in Engineering Design, Analysis & Manufacturing, 1(2):73–87, 1987.

    Google Scholar 

  • McKernan, T. J., and Jayaraman, B., CobWeb: A Constraint-based XML for the Web, Department of Computer Science, University at Buffalo, 2000.

    Google Scholar 

  • Menon R., Loh H. T., and Sathiyakeerthi S., Analyzing textual databases using Data Mining to enable fast product development processes, Reliability Engineering and System Safety, in press, 2005.

    Google Scholar 

  • Mitchell T., Mahadevan S., and Steinberg L., LEAP: A learning apprentice for VLSI design, Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA, pp 573–580. Morgan Kaufmann, San Mateo, CA, 1985.

    Google Scholar 

  • Pine B. J., Mass Customization: The New Frontier in Business Competition, Harvard Business School Press, Boston, Mass., 1993.

    Google Scholar 

  • Potter S., Darlington M. J., Culley S. J., and Chawdhry P. K., Design synthesis knowledge and inductive machine learning, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 15, 233–249, 2001.

    Article  MATH  Google Scholar 

  • Provost F., Fawcett T., and Kohavi R., The case against accuracy estimation for comparing induction algorithms, Proceedings of the Fifteenth International Conference on Machine Learning, pp 445–453, Morgan Kaufmann, San Francisco, CA, 1998.

    Google Scholar 

  • Reich Y., Design knowledge acquisition: Task analysis and a partial implementation, Knowledge Acquisition, 3(3):237–254, 1991.

    Article  Google Scholar 

  • Reich Y., The development of Bridger: A methodological study of research on the use of machine learning in design, Artificial Intelligence in Engineering, 8(3):217–31, 1993.

    Article  Google Scholar 

  • Reich Y., Micro and Macro Perspectives of Multistrategy Learning, In Machine Learning: A Multistrategy Approach, Michalski R. S. and Tecuci G. (eds.). pp 379–401, Morgan Kaufmann, San Francisco, 1994.

    Google Scholar 

  • Reich Y., Measuring the value of knowledge, International Journal of Human-Computer Studies, 42(1): 3–30, 1995.

    Article  Google Scholar 

  • Reich Y., Learning in design: from characterising dimensions to working systems, Artificial Intelligence in Engineering Design, Analysis & Manufacturing, 12:161–172, 1998.

    Google Scholar 

  • Reich Y., Life cycle management of information and decisions for system analyses. Mechanical Systems and Signal Processing, 15(3):513–527, 2001.

    Article  Google Scholar 

  • Reich Y. and Barai S. V., Evaluating machine learning models for engineering problems, Artificial Intelligence in Engineering, 13(3):257–272, 1999.

    Article  Google Scholar 

  • Reich Y. and Barai S. V., A methodology for building neural networks models from empirical engineering data. Engineering Applications of Artificial Intelligence, 13(6):685–694. 2000.

    Article  Google Scholar 

  • Reich Y. and Fenves S. J., Integration of Generic Learning Tasks, Technical Report EDRC 12-28-89, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA 15213, 1988.

    Google Scholar 

  • Reich Y. and Fenves S. J., The potential of machine learning techniques for expert systems, Artificial Intelligence for Engineering Design, Analysis & Manufacturing, 3(3):175–193, 1989.

    Google Scholar 

  • Reich Y. and Fenves S. J., The formation and use of abstract concepts in design, in Concept Formation: Knowledge and Experience in Unsupervised Learning, D. H. J. Fisher. M. J. Pazzani & P. Langley, eds., Morgan Kaufmann, Los Altos, CA, pp 323–353, 1992a.

    Google Scholar 

  • Reich Y. and Fenves S. J., Inductive learning of synthesis knowledge, International Journal of Expert Systems: Research and Applications, 5(4):275–97, 1992b.

    Article  Google Scholar 

  • Reich Y. and Fenves S J., A system that learns to design cable slayed bridges, Journal of Structural Engineering, ASCE, 121(7):1090–1100, 1995.

    Article  Google Scholar 

  • Reich Y, Konda S., Levy S. N., Monarch I., and Subrahmanian E., New roles for machine learning in design. Artificial Intelligence in Engineering, 8(3): 165–181, 1993.

    Article  Google Scholar 

  • Romanowski C. J. and Nagi R., A Data Mining-based engineering design support system: a research agenda, in Data Mining for design and manufacturing: methods and applications (ed. Braha D.), Kluwer Academic Publishers, Norwell, MA, USA, pp 161–178, 2001.

    Google Scholar 

  • Romanowski C. J. and Nagi R., A Data Mining approach to forming generic bills of materials in support of variant design activities, ASME Journal of Computing and Information Science in Engineering, 4(4), 2004.

    Google Scholar 

  • Romanowski C J. and Nagi R., On comparing bills of materials: A similarity/distance measure for unordered trees. IEEE Transactions on Systems, Man, and Cybernetics, Part A, in press, 2005.

    Google Scholar 

  • Schimm G., Mining exact models of concurrent workflows, Computers in Industry, 53(3):265–281, 2004.

    Article  Google Scholar 

  • Schwabacher M, Ellman T., Hirsh H., Learning to set up numerical optimizations of engineering designs, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12:173–192, 1998.

    Google Scholar 

  • Sim S. K. and Duffy A. H. B., A foundation for machine learning in design, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 12:193–209, 1998.

    Google Scholar 

  • Simpson T., Peplinski J, Koch P., and Allen J., Metamodels for Computer-Based Engineering Design: Survey and Recommendations, Engineering with Computers, 17(2)129–150, 2001.

    Article  MATH  Google Scholar 

  • Subrahmanian E., Konda S. L., Levy S. N., Reich Y., Westerberg A. W., and Monarch I. A.. Equations aren’t enough: Informal modeling in design, Artificial Intelligence in Engineering Design, Analysis, and Manufacturing, 7(4):257–274, 1993.

    Article  Google Scholar 

  • Williams, T., The need for new paradigms for complex projects, International Journal of Project Management, 17(5):269–273, 1999.

    Article  Google Scholar 

  • Yamashita Y., Supervised learning for the analysis of process operational data, Computers & Chemical Engineering, 24:471–474, 2000.

    Article  Google Scholar 

  • Yerramareddy S., Tcheng D. K., Lu S. C.-Y., and Assanis D. N., Creating and using models for engineering design: A machine-learning approach, IEEE Transactions on Intelligent Systems, 7(3):52–59, 1992.

    Google Scholar 

  • Zarka J. and Hablot J. M., Learning expert systems in numerical analysis of structures, in Expert Systems in Structural Safety Assessment (Berlin, 1989), A. S. Jovarwvic, K. F. Kussmaul, A. C. Lucia & P. P. Bonissone, eds., Springer-Verlag, Berlin, pp 305–314, 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Reich, Y. (2005). Data Mining of Design Products and Processes. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_55

Download citation

  • DOI: https://doi.org/10.1007/0-387-25465-X_55

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24435-8

  • Online ISBN: 978-0-387-25465-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics