A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Industrial application of fuzzy systems: adaptive fuzzy control of solder paste stencil printing
Information Sciences: an International Journal
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Cluster Validation with Generalized Dunn's Indices
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Using PCA to model shape for process control
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
A Compact and Accurate Model for Classification
IEEE Transactions on Knowledge and Data Engineering
A hybrid sales forecasting system based on clustering and decision trees
Decision Support Systems
A case-based reasoning system for PCB principal process parameter identification
Expert Systems with Applications: An International Journal
A survey of kernel and spectral methods for clustering
Pattern Recognition
Modeling and optimization of stencil printing operations: A comparison study
Computers and Industrial Engineering
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
A two-stage clustering approach for multi-region segmentation
Expert Systems with Applications: An International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian framework for multilead SMD post-placement quality inspection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two-stage clustering via neural networks
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Journal of Intelligent Manufacturing
Expert Systems with Applications: An International Journal
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Soldering failures lead to considerable manufacturing costs in the electronics assembly industry. Soldering problems can be caused by improper parameter settings during paste stencil printing, component placement, the solder reflow process or combinations thereof in surface mount assembly (SMA). Data mining has emerged as one of the most dynamic fields in processing large manufacturing databases and process knowledge extraction. In this study, the integration of a probabilistic network of the SMA line and a hybrid data mining approach is employed to identify soldering defect patterns, classify soldering quality, and predict new instances according to significant process inputs. The hybrid data mining approach uses a two-stage clustering method that utilizes the self-organizing map (SOM) to derive the preliminary number of clusters and their centroids from the statistical process control (SPC) database, followed by the use of K-means to precisely classify instances into definite classes of soldering quality. The See5 induction system is then applied to induce the decision tree and ruleset to elucidate associations among the defect patterns, process parameters, and assembly yield. Finally, visual C++ programming codes are implemented for both production rule retrieval and graphical user interface establishment. The effectiveness of the proposed classifier is illustrated through a real-world application to resolve practical manufacturing problems.