Query-constraint-based association rule mining from diverse clinical datasets in the national sleep research resource

Abeysinghe, Rashmie; Cui, Licong

doi:10.1109/bibm.2017.8217834

Cited by 10 publications

(8 citation statements)

References 9 publications

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“…We performed a preliminary study [ 31 ] on query-constraint-based ARM in NSRR which motivated this work. However, in [ 31 ] we did not perform any post-processing on the results. The results contained a lot of general as well as subsumed rules.…”

Section: Discussionmentioning

confidence: 99%

Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

Abeysinghe¹,

Cui²

2018

BMC Med Inform Decis Mak

Self Cite

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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 general characteristics.MethodsWe introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of multiple, diverse clinical datasets in the National Sleep Research Resource (NSRR). QARM enables rule mining on a subset of data items satisfying a query constraint. We first perform a series of data-preprocessing steps including variable selection, merging semantically similar variables, combining multiple-visit data, and data transformation. We use Top-k Non-Redundant (TNR) ARM algorithm to generate association rules. Then we remove general and subsumed rules so that unique and non-redundant rules are resulted for a particular query constraint.ResultsApplying QARM on five datasets from NSRR obtained a total of 2517 association rules with a minimum confidence of 60% (using top 100 rules for each query constraint). The results show that merging similar variables could avoid uninteresting rules. Also, removing general and subsumed rules resulted in a more concise and interesting set of rules.ConclusionsQARM shows the potential to support exploratory analysis of large biomedical datasets. It is also shown as a useful method to reduce the number of uninteresting association rules generated from imbalanced datasets. A preliminary literature-based analysis showed that some association rules have supporting evidence from biomedical literature, while others without literature-based evidence may serve as the candidates for new hypotheses to explore and investigate. Together with literature-based evidence, the association rules mined over the NSRR clinical datasets may be used to support clinical decisions for sleep-related problems.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0633-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

Abeysinghe¹,

Cui²

2018

BMC Med Inform Decis Mak

Self Cite

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“…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%

“…To satisfy the requirements of users, a series of frequency-based methods that can search for specific goal items were proposed. 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%

TUSQ: Targeted High-Utility Sequence Querying

Zhang¹,

Du²,

Dai³

et al. 2021

Preprint

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Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds suffix remain utility and terminated descendants utility as well as a vertical Last Instance Table structure. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.

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“…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%

“…Researchers have defined this interesting task as target-oriented pattern mining [17]. To date, several studies have been conducted on target-based pattern mining, such as target-oriented frequent itemset querying [18], target-based association rule mining [19], [20], and targeted sequential pattern querying [21]- [23]. As stated previously, customers are usually not interested in receiving all discount messages according to the recommender system.…”

Section: Introductionmentioning

confidence: 99%

TargetUM: Targeted High-Utility Itemset Querying

Miao¹,

Wan²,

Gan³

et al. 2021

Preprint

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Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (minUtil) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of TargetUM is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.

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Query-constraint-based association rule mining from diverse clinical datasets in the national sleep research resource

Cited by 10 publications

References 9 publications

Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

TUSQ: Targeted High-Utility Sequence Querying

TargetUM: Targeted High-Utility Itemset Querying

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