Subgroup Discovery with CN2-SD

By Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski. 2004

441 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

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.”
Searching for rules to detect defective modules: A subgroup discovery approach (2012, methodology): “These datasets were created from projects carried out at NASA3.”
Evaluation Measures for Multi-class Subgroup Discovery (2009, methodology): “In [1] this trade-off was achieved using weighted relative accuracy, but their CN2-SD algorithm is restricted to two classes.”
An overview on subgroup discovery: foundations and applications (2011, background): “– Coverage: It measures the percentage of examples covered on average [85].”
Identifying miRNAs, targets and functions (2012, methodology): “The Pearson’s correlation coefficients are further calculated for miRNAs output from CN2-SD, and only highly correlated miRNAs are maintained for final MRMs.”
A statistical approach to rule learning (2006, methodology): “Simplicity has always been an important issue in rule learning (see for instance Weiss and Indurkhya, 1993; Lavrač et al., 2004) and decision tree learning (for example Bohanec and Bratko, 1994).”
Contrast Set Mining for Distinguishing Between Similar Diseases (2007, methodology): “There are several algorithms that are adaptations of rule learners to perform the subgroup discovery task: SD [4], CN2-SD [10] and Apriori-SD [6].”
with Subgroup (2015, unknown): “φWRMOE( fbase,G) = |G| N · (∑i∈G zOE i |G| − ∑ N i=1 zOE i N ) (4)”
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining (2009, methodology): “Examples of such heuristics are the weighted relative accuracy (Equation 2, see Lavraˇc et al., 2004b) and the generalization Weighted relative accuracy, known from subgroup discovery, and support difference between groups, used in contrast set mining, are related as follows: 4 WRAcc ( X , Y ) = =…”
An experimental evaluation of simplicity in rule learning (2008, unknown): “(17), who modified the CN2 algorithm in order to induce smaller and more meaningful rule sets with the individual rules having higher support and significance.”
Cluster-grouping: from subgroup discovery to clustering (2004, methodology): “[1, 34, 28, 12, 38] These systems all possess a local pattern mining or rule learning component, which raises the question as to whether there exists a unified or universal local pattern mining approach that can be used across these systems.”