Integrating data mining techniques and design information management for failure prevention

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2253, Issue , 2001, Pages 475-480

  Kato, Yoshikiyo ^a   Yairi, Takehisa ^a   Hori, Koichi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DESIGN; FAULT DETECTION; INFORMATION MANAGEMENT;

ANOMALY-HANDLING; DESIGN RATIONALE; DEVELOPMENT PROCESS; FAILURE PREVENTION; HOUSE KEEPING; MINING ASSOCIATIONS;

DATA MINING;

EID: 0010326674     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45548-5_65     Document Type: Conference Paper

References (4)

1. Hatamura, Y.: Invitation to Failure Science (Shippaigaku no susume). Ko-dansha, Tokyo (2000)
2. Yairi, T., Kato, Y., Hori, K.: Fault Detection by Mining Association Rules from House-keeping Data. Proc. of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space (2001)
3. Gruber, T. R., Russel, D. M.: Design Knowledge and Design Rationale: A Framework for Representation, Capture and Use. Technical Report. Knowledge Systems Laboratory, Computer Science Department, Stanford University (1991)
4. Shipman, F. M., McCall, R. J.: Integrating Different Perspectives on Design Rationale: Supporting the Emergence of Design Rationale from Design Communication. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 11 (1996) 141-154