<b>arules</b>- A Computational Environment for Mining Association Rules and Frequent Item Sets

Hahsler, Michael; Grün, Bettina; Hornik, Kurt

doi:10.18637/jss.v014.i15

Cited by 449 publications

(364 citation statements)

References 24 publications

Supporting

Mentioning

357

Contrasting

Unclassified

Order By: Relevance

“…Rule generation was performed on discretized data using the arules package ( [16], v1.0-14), with minimal support = 0.001, confidence = 0.001, and a rule size of two. A two-item rule size was necessitated by the NHANES study design as not every environmental exposure was measured in each subject.…”

Section: Methodsmentioning

confidence: 99%

“…The apriori algorithm [6,8,16] is a breadth-first search of an itemset that can be used to identify association rules describing the implication between the item(s) on the left-hand side (LHS, antecedent) and those on the right-hand side (RHS, consequent). Association rules have been used to mine medical records data to identify potential correlations from frequent co-occurring features, for example incidence of a side effect and a prescribed drug [9,17,22,21,25].…”

Section: Related Workmentioning

confidence: 99%

“…This treatment of the data facilitates the use of contrast tables (1) for the examination of a specific exposure ⇒ health rule. Equations (2.1) to (2.4) define the key parameters used for describing the rule [16,31]. Support (S, eq.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining

Bell

Edwards

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

Health effects are unknown for the vast majority of the >83,000 chemicals in commerce, creating challenges in balancing industrial needs against the complex landscape of health susceptibilities and exposures. The National Health and Nutritional Examination Survey (NHANES), a large-scale survey aimed at determining the prevalence and risk factors of major diseases, is increasingly used to postulate relationships between chemicals and adverse health effects in the U.S. population. The interpretation of these studies is complicated, however, by the ad hoc data mining approaches typically employed.Here we describe the use of frequent itemset mining for identifying exposure ⇒ health associations in data from the NHANES 2005-2006 cycle. From 9,440 dichotomized samples, 983 two-itemset rules were generated describing associations between markers of health and environmental exposure (lift >1, response threshold >97.5th quantile). A case study using parathyroid hormone levels to develop exposure-health effect hypotheses is presented. This case study demonstrates how association rules can be used in data mining to facilitate hypothesis development and improve traditional regression models by identification of potentially confounding variables even in the presence of missing information. Our approach is designed to enable more effec- * This project was supported in part by an appointment to the Internship/Research Participation Program at the Office of Research and Development, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. We thank Drs Lyle Burgoon, Jennifer Richmond-Bryant, and Jon Sobus for helpful discussion and review of early drafts of the paper. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency. tive knowledge discovery of potential health impacts of environmental chemicals by facilitating comprehensive data mining and meta-analysis of the NHANES dataset. Long-term, our representation of the information allows for integration with other disparate data, such as known biological pathways, to address the current data gaps.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining

Bell

Edwards

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…See Hahsler et al (2005) for a more recent discussion of mining of association rules and a computer package providing tools. The problem of predicting the next step in a journey conditional on the observed history is also much studied, but is not relevant here.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Time-Weighted Multi-Touch Attribution and Channel Relevance in the Customer Journey to Online Purchase

Wooff

Anderson

2014

Journal of Statistical Theory and Practice

View full text Add to dashboard Cite

. (2015) 'Time-weighted multi-touch attribution and channel relevance in the customer journey to online purchase.', Journal of statistical theory and practice., 9 (2). pp. 227-249. Further information on publisher's website:http://dx.doi.org/10. 1080/15598608.2013.862753 Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a service to authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to this version also. PLEASE SCROLL DOWN FOR ARTICLETaylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor AbstractWe address statistical issues in attributing revenue to marketing channels and inferring the importance of individual channels in customer journeys towards an online purchase. We describe the relevant data structures and introduce an example. We suggest an asymmetric bathtub shape as appropriate for time-weighted revenue attribution to the customer journey, provide an algorithm, and illustrate the method. We suggest a modification to this method when there is independent information available on the relative values of the channels. To infer channel importance, we employ sequential data analysis ideas and restrict to data which ends in a purchase. We propose metrics for source, intermediary, and destination channels based on twoand three-step transitions in fragments of the customer journey. We comment on the practicalities of formal hypothesis testing. We illustrate the ideas and computations using data from a major UK online retailer. Finally, we compare the revenue attributions suggested by the methods in this paper with several common attribution methods.

show abstract

“…2) Difference of Confidence (DOC) [7] This correlation measure is to compare the posterior and the prior confidence of an association rule [6]. Since the former should differ considerably from the latter to make the rule interesting, which means the occurrence of A has a significant impact on the occurrence of B.…”

Section: Correlation Analysismentioning

confidence: 99%

A Study of the Relationship between Credits in the LEED-EB&OM Green Building Rating System

Cheng¹,

Ma²

2013

IJET

View full text Add to dashboard Cite

Abstract-LEED (Leadership in Energy and EnvironmentalDesign) is a credit-based green building rating system. Considering that a better understanding of the relationships between credits would help managers better achieve green building certification, this study analyzed 1381 projects that have been certified in LEED-Existing Building versions 2008 and 2009. The credits achieved by those projects were analyzed using data mining techniques to discover hidden inter-relationships and the effects on high-scoring sustainable design strategies. The data mining results were compared with the credit pairs provided by LEED AP consultants from the engineering perspective. Additional hidden credit pairs were also discovered.

show abstract

arules- A Computational Environment for Mining Association Rules and Frequent Item Sets

Cited by 449 publications

References 24 publications

Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining

Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining

Time-Weighted Multi-Touch Attribution and Channel Relevance in the Customer Journey to Online Purchase

A Study of the Relationship between Credits in the LEED-EB&OM Green Building Rating System

Contact Info

Product

Resources

About