Detecting change in categorical data: mining contrast sets

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Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Similarly, algorithms for problems such as subgroup discovery [24, 33, 40, 46], contrast set learning [3, 4, 30], and emerging pattern mining [17, 19] identify sets of rules to describe the relationships among variables and discover interesting patterns in the data.”
Detecting Group Differences: Mining Contrast Sets (2001, methodology): “If G is large we can use χ2 bounds (Bay and Pazzani, 1999) that can be found in linear time.”
Choosing the Right Lens: Finding What is Interesting in Data Mining (2007, background): “Surprisingness A pattern contradicts a person’s existing knowledge or expectations Bay and Pazzani, 1999 [ 6 ]; Carvalho and Freitas, 2000 [8] Liu et al., 1997 [27]; Liu et al., 1999 [28]; Silberschatz and Tuzhilin, 1995 [40]; Silberschatz and Tuzhilin, 1996 [41];”
Rules for contrast sets (2010, unknown): “STUCCO [3,4] is the standard approach to this mining task.”
Graph based anomaly detection and description: a survey (2014, methodology): “2003); change detection in categorical data (Bay and Pazzani 1999); StreamKrimp, an MDL-based algorithm (Leeuwen and Siebes 2008); detection of disease outbreaks (Wong et al.”