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Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life datasets, this limits the recovery of frequent itemset patterns as they are fragmented due to random noise and other errors in the data. Hence, a number of methods have been(More)
In this paper, we study methods to identify differential coexpression patterns in case-control gene expression data. A differential coexpression pattern consists of a set of genes that have substantially different levels of coherence of their expression profiles across the two sample-classes, i.e., highly coherent in one class, but not in the other.(More)
—Discriminative patterns can provide valuable insights into data sets with class labels, that may not be available from the individual features or the predictive models built using them. Most existing approaches work efficiently for sparse or low-dimensional data sets. However, for dense and high-dimensional data sets, they have to use high thresholds to(More)
Discriminative patterns are association patterns that occur with disproportionate frequency in some classes versus others, and have been studied under names such as emerging patterns and contrast sets. Such patterns have demonstrated considerable value for classification and subgroup discovery, but a detailed understanding of the types of interactions among(More)
Generative models of pattern individuality attempt to represent the distribution of observed quantitative features, e.g., by learning parameters from a database, and then use such distributions to determine the probability of two random patterns being the same. Considering fingerprint patterns , Gaussian distributions have been previously used for minutiae(More)
Discriminative pattern mining looks for association patterns that occur more frequently in one class than another and has important applications in many areas including finding biomarkers in biomedical data. However, finding such patterns is challenging because higher order combinations of variables may show high discrimination even when single variables or(More)
BACKGROUND Pandemic influenza A (H1N1) virus emerged in North America in April 2009 and spread globally. We describe the epidemiology and public health response to the first known outbreak of 2009 H1N1 in a train, which occurred in June 2009 in China. METHODS After 2 provinces provided initial reports of 2009 H1N1 infection in 2 persons who had travelled(More)
Association analysis is one of the most popular analysis paradigms in data mining. Despite the solid foundation of association analysis and its potential applications, this group of techniques is not as widely used as classification and clustering, especially in the domain of bioinformatics and computational biology. In this paper, we present different(More)
Association analysis is one of the most popular analysis paradigms in data mining. In this paper, we present different types of association patterns and discuss some of their applications in bioinformatics. We present a case study showing the usefulness of association analysis-based techniques for pre-processing protein interaction networks. Finally, we(More)