Noise modelling and evaluating learning from examples
Artificial Intelligence
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Information quality benchmarks: product and service performance
Communications of the ACM - Supporting community and building social capital
Communications of the ACM - Supporting community and building social capital
Emerging trends in business analytics
Communications of the ACM - Evolving data mining into solutions for insights
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Using Data Quality Measures in Decision-Making Algorithms
IEEE Expert: Intelligent Systems and Their Applications
AIMQ: a methodology for information quality assessment
Information and Management
Machine Learning
Modeling Completeness versus Consistency Tradeoffs in Information Decision Contexts
IEEE Transactions on Knowledge and Data Engineering
The Effects of Training Set Size on Decision Tree Complexity
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Journey to Data Quality
Supporting data quality management in decision-making
Decision Support Systems
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Integrated decision support systems: A data warehousing perspective
Decision Support Systems
A data quality metamodel extension to CWM
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
Referential integrity quality metrics
Decision Support Systems
A Procedure to Develop Metrics for Currency and its Application in CRM
Journal of Data and Information Quality (JDIQ)
Competing on Analytics: The New Science of Winning
Competing on Analytics: The New Science of Winning
On learning algorithm selection for classification
Applied Soft Computing
Dual Assessment of Data Quality in Customer Databases
Journal of Data and Information Quality (JDIQ)
Validating cluster structures in data mining tasks
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Data quality: Setting organizational policies
Decision Support Systems
Data & Knowledge Engineering
Enhancing scientific information systems with semantic annotations
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A provenance-based approach to evaluate data quality in eScience
International Journal of Metadata, Semantics and Ontologies
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Data quality remains a persistent problem in practice and a challenge for research. In this study we focus on the four dimensions of data quality noted as the most important to information consumers, namely accuracy, completeness, consistency, and timeliness. These dimensions are of particular concern for operational systems, and most importantly for data warehouses, which are often used as the primary data source for analyses such as classification, a general type of data mining. However, the definitions and conceptual models of these dimensions have not been collectively considered with respect to data mining in general or classification in particular. Nor have they been considered for problem complexity. Conversely, these four dimensions of data quality have only been indirectly addressed by data mining research. Using definitions and constructs of data quality dimensions, our research evaluates the effects of both data quality and problem complexity on generated data and tests the results in a real-world case. Six different classification outcomes selected from the spectrum of classification algorithms show that data quality and problem complexity have significant main and interaction effects. From the findings of significant effects, the economics of higher data quality are evaluated for a frequent application of classification and illustrated by the real-world case.