AssocExplorer

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

doi:10.1145/2339530.2339774

Cited by 16 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Visualization techniques, such as scatter plot (Liu et al, 2012) and node-link graph (Leung et al, 2008), were used for exploring and explaining the association rules. These approaches focus on the final association rule result.…”

Section: Introductionmentioning

confidence: 99%

A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data

Valêncio¹,

Morais²,

Fortes³

et al. 2018

Journal of Computer Science

View full text Add to dashboard Cite

Data mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multirelational data mining algorithm based on association rules, called TBMR-Radix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not.

show abstract

InterSet: Interactive Redescription Set Exploration

Mihelčić

Šmuc

2016

Discovery Science

View full text Add to dashboard Cite

AssocExplorer

Cited by 16 publications

References 4 publications

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

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

A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data

InterSet: Interactive Redescription Set Exploration

Contact Info

Product

Resources

About