Efficient mining of emerging patterns: discovering trends and differences

By Guozhu Dong, Jinyan Li. 1999

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “A contrast pattern (cp) for a class Ci is a pattern p whereby the support of p forCi is significantly higher than any support of p for every class other than Ci [7], [8], [110], [135], [136].”
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.”
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).”
Merging chrominance and luminance in early, medium, and late fusion using Convolutional Neural Networks (2015, background): “, 1993] or Jumping Emerging Patterns (JEPs) [Dong and Li, 1999].”
An Approach to Pose-Based Action Recognition (2013, methodology): “We obtain sets of spatial-part-sets for each action class using Contrast Mining techniques[6].”
What Did I Do Wrong in My MOBA Game? Mining Patterns Discriminating Deviant Behaviours (2016, unknown): “Secondly, we explain how to discover Emerging patterns that is, itemsets that discriminate a class label [17].”
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.”
Correlated itemset mining in ROC space: a constraint programming approach (2009, unknown): “Asymmetric scoring functions, that is, functions in which only one of f(|T |, 0) and f(0, |T |) is a local maximum, are also common; examples include Laplace value, confidence and conviction; furthermore, the growth rate used in emerging patterns is also asymmetric [12].”
Fuzzy emerging patterns for classifying hard domains (2011, background): “Dong and Li [4] boost research about pattern-based classifiers with the development of emerging patterns (EPs).”
Discovering frequent patterns in sensitive data (2010, background): “It forms the basis of several data mining tasks such as mining association rules [2], detecting correlations, discovering emerging patterns [7], etc.”
Detecting Group Differences: Mining Contrast Sets (2001, methodology): “We ignore this as we are using the data only as a computational test (this violation is also ignored in (Silverstein et al., 1998; Dong & Li, 1999; Brin et al., 1997) etc.).”
DATA MINING TECHNIQUES (2003, background): “Instead of focusing on distance, DeEPs focuses on the so-called emerging patterns [24].”
Towards Integrative Machine Learning and Knowledge Extraction (2017, methodology): “We explored three such services: googlemaps [33], pygeocoder [34], which is built on googlemaps, and lastly, issuing REST APIs to Google Maps [21], e.”
An overview on subgroup discovery: foundations and applications (2011, background): “– Emerging Pattern Mining [38] defined as “patterns whose frequencies in two classes differ by a large ratio”.”