Applications eligible for data mining

Washio, Takashi

doi:10.1016/j.aei.2007.01.001

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In recent years, data mining has been used in engineering as well (Washio 2007;Alonso et al 2004;Grossman et al 2001). Examples of applications include oil production prediction (Nguyen and Chan 1999), traffic pattern recognition (Yan et al 2005), composite joint behavior (Shirazi Kia et al 2005) and joint damage assessment (Yun et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Improving Knowledge of Structural System Behavior through Multiple Models

Smith

Saitta

2008

J. Struct. Eng.

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A system identification and model updating methodology that accounts for factors influencing the reliability of identification is proposed. An important aspect of this methodology is the generation of a population of candidate models. This paper presents an analysis of error sources that are used to define model populations. A case study illustrates the need for such an approach even when a single conservative model has been appropriate for design. Data mining techniques such as principal component analysis and k-means clustering combined to interpret model predictions. These methods are useful for estimating the dependability of system identification.

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Section: Introductionmentioning

confidence: 99%

Improving Knowledge of Structural System Behavior through Multiple Models

Smith

Saitta

2008

J. Struct. Eng.

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“…Knowledge Discovery in Databases (KDD) has become one of the fastest growing research topics in mathematics and computer science, because the ability to continually change and acquire new understanding is a driving force for its applications (Liao, Chu, & Hsiao, 2012;Washio, 2007). For example data mining have served in the search and retrieval of computer-aided design elements (Liu, McMahon, Ramani, & Schaefer, 2011).…”

Section: Introductionmentioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

132

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To cite this version:Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget. Mining association rules for the quality improvement of the production process. Expert Systems with Applications, Elsevier, 2013, vol. 40, pp. 1034-1045 b s t r a c tAcademics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.

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Applications eligible for data mining

Cited by 2 publications

References 12 publications

Improving Knowledge of Structural System Behavior through Multiple Models

Improving Knowledge of Structural System Behavior through Multiple Models

Mining association rules for the quality improvement of the production process

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