Dynamic programming algorithm optimization for spoken word recognition
Readings in speech recognition
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
Design rationale: concepts, techniques, and use
Design rationale: concepts, techniques, and use
Automated capture of rationale for the detailed design process
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Case-Based Reasoning in Design
Case-Based Reasoning in Design
Data mining for knowledge acquisition in engineering design
Data mining for design and manufacturing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Design History Systems: Data Models & Prototype Implementation
Proceedings of the IFIP TC5 WG5.2 Third Workshop on Knowledge Intensive CAD
Acquiring engineering knowledge from design processes
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Comparison between objective interestingness measures and real human interest in medical data mining
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Editorial: Applications eligible for data mining
Advanced Engineering Informatics
MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining
Advanced Engineering Informatics
Software Engineering Using RATionale
Journal of Systems and Software
Advanced Engineering Informatics
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Capturing engineering knowledge and managing it effectively is important for enterprises to stay competitive in today's global market. In our research, we focus on specific engineering knowledge, called design procedures, and attempt to develop effective ways to capture this operational knowledge from the design events monitored during design process. We proposed a novel method called Information Value based Mining for Sequential Pattern, or VMSP for short. VMSP does not require any predefined design-task-specific rules for classification of design events. The basic idea of VMSP is to treat any list of monitored design events as a sequential pattern and search for most frequent and informative sequential patterns. VMSP automatically generates candidate templates of sequential patterns, and then identify valuable patterns as design operations based on the interestingness we set. The interestingness is a synthesized index of the two evaluation criteria: One is intrinsic value representing how many design events constitute the template; and the other is extrinsic value indicating how often the sequence appears in the design process. We have evaluated VMSP through two case studies using real CAD operation data recorded through gear design and automotive door design processes, respectively. A case study using synthetic data was also carried out to test the scalability of the proposed algorithm.