Summarizing categorical data by clustering attributes
By Michael Mampaey, Jilles Vreeken. 2011
32 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing
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In Computational Theory and Mathematics
In Information Systems
In Signal Processing
Citations in the corpus
- Interpreting Classifiers through Attribute Interactions in Datasets (2017, methodology): “Mampaey & Vreeken (2013) partition attributes by a greedy hierarchical clustering algorithm based on Minimum Description Length (MDL).”
- Finding Statistically Significant Attribute Interactions (2016, background): “[20] investigated finding maximally dependent successively ordered attributes while [15] clustered correlated attributes into groups.”
- ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data (2013, methodology): “Change point analysis is also used to detect credit card fraud (Bolton and Hand, 2002) and other anomalies (Akoglu and Faloutsos, 2010; Sequeira and Zaki, 2002); and for data classification in data mining (Mampaey and Vreeken, 2011).”
- A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “Change point analysis is also used to detect credit card fraud (Bolton and Hand 2002) and other anomalies (Sequeira and Zaki 2002; Akoglu and Faloutsos 2010); and for data classification in data mining (Mampaey and Vreeken 2011).”