AssocExplorer

Liu, Guimei; Suchitra, Andre; Zhang, Haojun; Feng, Mengling; Ng, See-Kiong; Wong, Limsoon

doi:10.1145/2339530.2339774

Cited by 18 publications

(5 citation statements)

References 4 publications

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“…Alternative visual metaphors that do not fit in these categories have also been proposed, for instance, using scatterplots of support vs. confidence [25,32] or dimensionality reduction layouts [33] to explore items or rules relationships, lacking the ability to understand "if-then" associations. Treemap-like visualizations have also been proposed to analyze rules [34], but it is only suitable for a very small number of rules.…”

Section: Related Workmentioning

confidence: 99%

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

2022

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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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Section: Related Workmentioning

confidence: 99%

“…G2-Comparative analysis. Comparing rules or groups of rules that share some property is the most common task of association rules analysis [15,19,32]. Our second goal is to support comparative analysis to reveal differences between rules regarding antecedents, consequents, and interesting measures.…”

Section: Armatrixmentioning

confidence: 99%

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

2022

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“…Several visualization techniques [3,4] have been presented to realize the representation of association rules. On the other hand, there have been few studies that simultaneously represents the distribution of numeric values as well as confidence and support values of association rules.…”

Section: Association Rule and Visualizationmentioning

confidence: 99%

Painting image browser applying an associate-rule-aware multidimensional data visualization technique

Kaneko

Komatsu

Itoh

et al. 2020

Vis. Comput. Ind. Biomed. Art

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Exploration of artworks is enjoyable but often time consuming. For example, it is not always easy to discover the favorite types of unknown painting works. It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists. This paper presents a painting image browser which assists the explorative discovery of user-interested painting works. The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images. This study assumes a large number of painting images are provided where categorical information (e.g., names of artists, created year) is assigned to the images. The presented system firstly calculates the feature values of the images as a preprocessing step. Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information. This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works. Our case study and user evaluation demonstrates the effectiveness of the presented image browser.

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“…Each of the mined rules is represented by an object consisting of a sphere perched on top of a cone. Liu et al (2013) used different colors to represent association rules that contain (or do not contain) a specific user-selected domain item in their As-socExplorer system. Note that all these systems focus on visualizing the results of association rule mining.…”

Section: Association Rule Visualizationmentioning

confidence: 99%

Interactive Visual Analytics of Databases and Frequent Sets

Leung

Carmichael

Johnstone

et al. 2013

International Journal of Information Retrieval Research

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In numerous real-life applications, large databases can be easily generated. Implicitly embedded in these databases is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for managing and retrieving useful information from these databases. Various algorithms have also been proposed for mining these databases to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. Many of the existing visualizers were not designed to visualize these mined frequent sets. In this journal article, an interactive visual analytic system is proposed for providing visual analytic solutions to the frequent set mining problem. The system enables the management, visualization, and advanced analysis of the original transaction databases as well as the frequent sets mined from these databases.

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AssocExplorer

Cited by 18 publications

References 4 publications

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

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

Painting image browser applying an associate-rule-aware multidimensional data visualization technique

Interactive Visual Analytics of Databases and Frequent Sets

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