Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A novel manufacturing defect detection method using data mining approach
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Decision analysis of data mining project based on Bayesian risk
Expert Systems with Applications: An International Journal
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The manufacturing quality can be evaluated only by considering the failure behavior of the product in the field. When relating manufacturing events to failure events, the main challenge is to master the huge number of combinations of both event types, of which each is only covered by a small number of occurrences. Additionally, this leads to the problem of selection of interesting findings – the appropriateness of the selection criterion for consequent decision making is a critical point. Another challenge is the necessity of mapping the process of manufacturing tests to a vector of variables characterizing the manufacturing process. The solution presented, focuses on correct rule generation and selection in the case of combinations with low coverage. Therefore statistical and decision theory approaches were used. The multiple hypothesis aspect of the rule set has also been considered. The application field was quality control of electronic units in automotive assembly, with thousands of variables observed.