ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics

Amongst the data mining techniques for exploratory analysis, association rule mining is a popular strategy given its ability to find causal rules between items to express regularities in a database. With large datasets, many rules can be generated, and visualization has shown to be instrumental in s...

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Main Authors: Rakshit Varu, Leonardo Christino, Fernando V. Paulovich
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/11/9/1344
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author Rakshit Varu
Leonardo Christino
Fernando V. Paulovich
author_facet Rakshit Varu
Leonardo Christino
Fernando V. Paulovich
author_sort Rakshit Varu
collection DOAJ
description Amongst the data mining techniques for exploratory analysis, association rule mining is a popular strategy given its ability to find causal rules between items to express regularities in a database. With large datasets, many rules can be generated, and visualization has shown to be instrumental in such scenarios. Despite the relative success, existing visual representations are limited and suffer from analytical capability and low interactive support issues. This paper presents ARMatrix, a visual analytics framework for the analysis of association rules based on an interactive item-to-rule matrix metaphor which aims to help users to navigate sets of rules and get insights about co-occurrence patterns. The usability of the proposed framework is illustrated using two user scenarios and then confirmed from the feedback received through a user test with 20 participants.
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spelling doaj.art-3adf5da7a8bb4ab1bd5d04ffb173b4b12023-11-23T08:02:15ZengMDPI AGElectronics2079-92922022-04-01119134410.3390/electronics11091344ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual AnalyticsRakshit Varu0Leonardo Christino1Fernando V. Paulovich2Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, CanadaFaculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, CanadaFaculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, CanadaAmongst the data mining techniques for exploratory analysis, association rule mining is a popular strategy given its ability to find causal rules between items to express regularities in a database. With large datasets, many rules can be generated, and visualization has shown to be instrumental in such scenarios. Despite the relative success, existing visual representations are limited and suffer from analytical capability and low interactive support issues. This paper presents ARMatrix, a visual analytics framework for the analysis of association rules based on an interactive item-to-rule matrix metaphor which aims to help users to navigate sets of rules and get insights about co-occurrence patterns. The usability of the proposed framework is illustrated using two user scenarios and then confirmed from the feedback received through a user test with 20 participants.https://www.mdpi.com/2079-9292/11/9/1344visual analyticsassociation rulesmatrix-based visualization
spellingShingle Rakshit Varu
Leonardo Christino
Fernando V. Paulovich
ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics
Electronics
visual analytics
association rules
matrix-based visualization
title ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics
title_full ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics
title_fullStr ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics
title_full_unstemmed ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics
title_short ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics
title_sort armatrix an interactive item to rule matrix for association rules visual analytics
topic visual analytics
association rules
matrix-based visualization
url https://www.mdpi.com/2079-9292/11/9/1344
work_keys_str_mv AT rakshitvaru armatrixaninteractiveitemtorulematrixforassociationrulesvisualanalytics
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AT fernandovpaulovich armatrixaninteractiveitemtorulematrixforassociationrulesvisualanalytics