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Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation
[article]
2019
arXiv
pre-print
The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the premise that context can aid in mitigating these issues. In a traditional data science lifecycle, context is not considered. Context-driven Data Science Lifecycle (C-DSL); the main
arXiv:1910.08670v1
fatcat:3rzimjokirfjhpswed3gjqrt6a