Data mining: manufacturing and service applications
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Summary
It is envisioned that the data-driven framework presented in the paper will enhance these applications of machine learning and data mining in industrial, medical, and pharmaceutical domains.
- Type
- article
- Published
- 2006-09-15
- Cited by
- 145
- References
- 42
- OpenAlex
- https://openalex.org/W2023369697
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15724830
Keywords
Computer science, Service (business), Data mining, Data science, Industrial engineering
References
- Analysis and Probability: Wavelets, Signals, Fractals
- The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains
- Information Integration for Concurrent Engineering (IICE) IDEF3 Process Description Capture Method Report
- Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
- Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
- A New Version of the Rule Induction System LERS
- Machine learning and data mining
- Data-mining-based methodology for the design of product families
- Data-Driven Constructive Induction
- Feature Selection via Mathematical Programming
- Mathematics Methods of Feature Selection in Pattern Recognition
- Feature Subset Selection Using a Genetic Algorithm
- A data mining approach for generation of control signatures
- Lazy Decision Trees
- DETECTION OF EVENTS CAUSING PLUGGAGE OF A COAL-FIRED BOILER: A DATA MINING APPROACH
- Autonomous decision-making: a data mining approach
- Data mining and genetic algorithm based gene/SNP selection
- Hypoplastic left heart syndrome: knowledge discovery with a data mining approach
- Predicting survival time for kidney dialysis patients: a data mining approach
- XML-based modeling of corporate memory
Cited by
- Enterprise Data Mining: A Review and Research Directions
- Applicability of decision trees in manufacturing industry
- Towards Meaningful and Valuable Data Mining Results in Organizations: Developing a framework for data mining that facilitates interaction between decision makers and data scientists to successfully apply data mining in a business context
- Manufacturing Quality Improvement with Data Mining Outlier Approach against Conventional Quality Measurements
- A new utility-emphasized analysis for stock trading rules
- Business Intelligence: Attribute and Feature Demand
- A big data approach for logistics trajectory discovery from RFID-enabled production data
- A study on the man-hour prediction system for shipbuilding
- On-line monitoring of power curves
- Adaptive process control based on a self-learning mechanism in autonomous manufacturing systems
- Data mining in business services
- Knowledge integration of distributed enterprises using cloud based big data analytics
- Data mining in manufacturing: Significance analysis of process parameters
- Data mining in design of products and production systems
- Prognostic analysis of defects in manufacturing
- Mining manufacturing databases to discover the effect of operation sequence on the product quality
- A New Approach to Generate Dispatching Rules for Two Machine Flow Shop Scheduling Using Data Mining
- Prediction of Wind Farm Power Ramp Rates: A Data-Mining
- Virtual models of indoor-air-quality sensors
- Applying data mining to manufacturing: the nature and implications
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