Detecting Group Differences: Mining Contrast Sets

By Stephen D. Bay, Michael J. Pazzani. Data mining and knowledge discovery, 2001

443 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5d2e9a18cd58be22ce2f6d6c485fbf0dc5a38cb6

How researchers see this paper

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

In Computational Theory and Mathematics

In Information Systems

In Signal Processing

Citations in the corpus

Mining significant graph patterns by leap search (2008, background): “The problem of evaluating statistical significance of patterns arises first in itemset mining, as illustrated by association rule discovery [1], k-optimal rule/pattern mining [24, 20, 25], emerging/contrast pattern discovery [8, 2], etc.”
Contrast Set Mining for Distinguishing Between Similar Diseases (2007, methodology): “Therefore, as is the case in other descriptive induction tasks, the goal of contrast set mining is to find only the descriptions that are “unexpected” and “most interesting” to the end-user [1].”
Rules for contrast sets (2010, unknown): “We also show evidence that the two-sided chi-squared test and the associated cutoff adjustment used in [4] are unappropriated for this task.”
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining (2009, methodology): “…be interpreted as class labels, so the data mining community, using the association rule learning perspective, adapted association rule learners like Apriori by Agrawal et al. (1996) to perform a task named contrast set mining (Bay and Pazzani, 2001) and emerging pattern mining (Dong and Li, 1999).”
Cluster-grouping: from subgroup discovery to clustering (2004, methodology): “A similar technique has been developed independently by Bay and Pazzani (2001) .”
Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, background): “Such understanding triggers the emergence of contrast pattern mining, such as emerging pattern [24, 25], jumping emerging patterns [41], and mining contrast sets [6].”
An overview on subgroup discovery: foundations and applications (2011, background): “– Contrast Set Mining [17], defined as " a conjunction of attribute-value pairs defined on groups with no attribute occurring more than once " .”