Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views

Sekhavat, Yoones A.; Hoeber, Orland

doi:10.4236/ijis.2013.31a005

Cited by 26 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Buono et al [BC05] used both graphs and parallel coordinates to get an overview as well as a detailed view of selected rules. Sekhavat et al [SH13] used matrices as overview of rules and graphs to analyze a selected subset of rules. Another approach includes a virtual arena [BGB03] where rules are represented as spheres positioned by the steps of an arena.…”

Section: Association Rule Visualizationmentioning

confidence: 99%

DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

Kakar

Qin

Rundensteiner

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

Adverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.

show abstract

Section: Association Rule Visualizationmentioning

confidence: 99%

DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

Kakar

Qin

Rundensteiner

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Based on human perception and intuition, graphical representation could be more illustrated to seek information by using the capabilities of the human visual system [2]. Such visual representations have been developed since the 1990s, allowing users to evaluate and interpret what rules from a large number of discovered rules.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

3D Matrix-Based Visualization System of Association Rules

Zhang

Chang

Ristaniemi

et al. 2017

2017 IEEE International Conference on Computer and Information Technology (CIT)

View full text Add to dashboard Cite

With the growing number of association rules, it becomes more and more difficult for users to explore interesting rules due to its nature complexity. Studies base on human perception and intuition show that graphical representation could be a better illustration of how to handle data by using the capabilities of the human visual system to seek information. The 3D matrixbased approach visualization system of association rules called 3DMVS was implemented in present study. The main visual representation employed the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed to generate new rules to reduce the dimension of the association rules, which will help users to find more important items in the new rule. Additionally, several interactions such as sorting, filtering, zoom and rotation, facilitate decision makers to explore the rules they are interested in various aspects. Finally, various evaluation techniques have been employed to assess the system from a logical reasoning point of view.

show abstract

“…Grzegorz Ilczuk and Alicja Wakulicz-Deja also proposed a rules visualization method based on iDecision trees [18]. The SARV tool provides the three synchronized views: the matrix view featuring a global view of the rules, the graph view illustrating relationships of the rules, and the detailed view of selected rules or items [19].…”

Section: Related Workmentioning

confidence: 99%

The new module for rules discovering and visualization for NovoSpark® Visualizer software

Pilipczuk

Cariowa²

2015

ELECTROTECHNICAL REVIEW

View full text Add to dashboard Cite

In this paper we present the new rough sets module for NovoSpark® Visualizer (NV) software. We describe the NV system architecture and the place of the new module in it. We also present the procedure of rough sets analysis with NV software. In addition an example of rules discovering and visualization is provided to evaluate the proposed module. The results show that useful rules are discovered efficiently from the data set. Streszczenie. W artykule zaprezentowano projekt nowego modułu do automatyzacji teorii zbiorów przybliżonych dla oprogramowania NovoSpark® Visualizer (NV). Opisano architekturę systemu oraz wskazano miejsce nowego modułu. Ponadto zaprezentowano przebieg procedury analizy i wizualizacji zbiorów przybliżonych w systemie. Przedstawiono przykład odkrywania i wizualizacji reguł za pomocą opracowanej procedury. W wyniku przeprowadzenia eksperymentu udało się otrzymać szereg użytecznych reguł decyzyjnych. (Nowy moduł odkrywania i wizualizacji reguł dla systemu NovoSpark® Visualizer).

show abstract

Visualizing Association Rules Using Linked Matrix,Graph, and Detail Views

Cited by 26 publications

References 25 publications

DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

3D Matrix-Based Visualization System of Association Rules

The new module for rules discovering and visualization for NovoSpark® Visualizer software

Contact Info

Product

Resources

About