2018
DOI: 10.1186/s12911-018-0633-7
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Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

Abstract: BackgroundAssociation Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture genera… Show more

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Cited by 12 publications
(7 citation statements)
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“…The association rule was evaluated by measures of support, con dence, and lift. The support of the rule is de ned as the percentage of transactions in T that contain both X and Y [24,25]. The support was calculated as follows:…”
Section: Methodsmentioning
confidence: 99%
“…The association rule was evaluated by measures of support, con dence, and lift. The support of the rule is de ned as the percentage of transactions in T that contain both X and Y [24,25]. The support was calculated as follows:…”
Section: Methodsmentioning
confidence: 99%
“…The fast and generic tool GFP-growth can determine the frequency of a given large list of itemsets, which serve as the targets, in a large dataset from an FP-tree [45] based on Target Itemset Tree. Recently, a query-constraint-based ARM model [17], [21] was developed for exploratory analysis of diverse clinical datasets integrated in the National Sleep Research Resource. It is important to consider the sequential ordering of itemsets in real-life applications.…”
Section: Target-oriented Queryingmentioning
confidence: 99%
“…Till now, target-oriented frequent itemset querying [14], association rule querying [15], [16], [17], and sequential pattern querying (SPQ) [18], [19], [20] have performed significant roles in querying in the database. The three target-oriented technologies can efficiently excavate patterns and rules involving a subset of certain items, such as targeted queries, and have shown significant potential in several real-life situations [21].…”
Section: Introductionmentioning
confidence: 99%
“…Its motivation is that utility-driven sequential pattern mining algorithms often obtain several useless patterns owing to exhaustive results. At present, mining algorithms with target-querying constraints have been applied in various applications [19], [40].…”
Section: Target Pattern Queryingmentioning
confidence: 99%