Outlier Detection in Heterogeneous Datasets using Automatic Tuple Expansion

By Clément Pit-Claudel, Zelda E. Mariet, R. Harding, Samuel Madden. 2016

63 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sanjay Krishnan
2. Eugene Wu
3. Mohammad Mahdavi
4. Felix Neutatz
5. Larysa Visengeriyeva
6. M. Franklin
7. Ihab F. Ilyas
8. Yinjun Wu

Citations in the corpus

• AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, methodology): “The default library consists of parametrized outlier detector methods from dBoost [35] and pyod [2].”
• Towards Automated Data Cleaning Workflows (2019, methodology): “We also have 15 error detection strategies generated by configuring 7 entirely different error detection tools: NADEEF [6], OpenRefine [28], and KATARA [5] are our rule, pattern, and knowledge base violation detection systems, respectively, and Histogram, Gaussian, Gaussian Mixture, and Partitioned ”
• Detecting Data Errors: Where are we and what needs to be done? (2016, unknown): “dBoost [27] is a novel framework that integrates several of the most widely applied outlier detection algorithms: histograms, Gaussian and multivariate Gaussian mixtures (GMM) [5].”