Using fine-tuned conditional probabilities for data transformation of nominal attributes.
Qiude LiQingyu XiongShengfen JiJunhao WenMin GaoYang YuRui XuPublished in: Pattern Recognit. Lett. (2019)
Keyphrases
- conditional probabilities
- data transformation
- fine tuned
- nominal attributes
- numerical attributes
- fine tuning
- data integration
- privacy preserving
- bayesian networks
- attribute values
- probability distribution
- probabilistic model
- privacy preserving data mining
- numeric attributes
- data quality
- data mining
- functional dependencies
- random variables
- domain specific
- subgroup discovery
- data warehousing
- dimensionality reduction
- class labels
- pattern recognition
- naive bayes
- real world
- training data
- association rules
- integrity constraints
- supervised learning
- databases
- business intelligence
- unsupervised learning
- low dimensional
- graphical models