A User Assistant for the Selection and Parameterization of the Visualizations in Visual Data Mining

Guettala, Abdelheq Et-Tahir; Bouali, Fatma; Guinot, Christiane; Venturini, Gilles

doi:10.1109/iv.2012.50

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The main difficulty is then to learn user preferences on data they cannot entirely view. Learning solutions proposed by systems such as VisAsist (Guettala et al, 2012) may be interesting, but rely on some a-priori and/or more sophisticated surrogate functions.…”

Section: Conclusion and Reflectionsmentioning

confidence: 99%

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

Boukhelifa

Bezerianos

Cancino

et al. 2017

Evolutionary Computation

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We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional data sets toward two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this article, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clearly predefined task, which examines how users leverage the system and how the system evolves to match their needs. While we previously showed that using EVE, domain experts were able to formulate interesting hypotheses and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating interactive evolutionary computation (IEC) techniques based on user study methodologies.

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Section: Conclusion and Reflectionsmentioning

confidence: 99%

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

Boukhelifa

Bezerianos

Cancino

et al. 2017

Evolutionary Computation

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“…For a given visual attribute, its list of possible data attributes contained only the data attributes that were compatible with the visual attribute. In the last version of VRMiner, instead of using the manual interface, users could also use VizAssist because the assistant was integrated in this system to help users configure their visualization (see [19]). …”

Section: User Evaluation 71 Overview Of the User Studymentioning

confidence: 99%

VizAssist: an interactive user assistant for visual data mining

2015

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“…Nos últimos anos, ferramentas vêm sendo projetadas para aprimorar a visualização dos dados, tais como as desenvolvidas por Guettala et al (2012), Akçaya e Büyüközkanb (2012) e Corteza e Embrechtsb (2013). Outros trabalhos já mencionam a incorporação das técnicas de visualização na análise de dados educacionais.…”

Section: Visualização De Dados: Conceitos E Trabalhos Relacionadosunclassified

Facilitando a Análise se Dados Educacionais através se Ferramentas se Visualização

Webber¹,

Cini²,

Lima³

2014

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As tecnologias de informação e comunicação permitiram a concepção de software educacionais em diversas áreas do conhecimento. No entanto, ainda cabe a cada professor acompanhar e monitorar interações, resoluções de problemas e todas as tarefas online de forma semi-manual, tornando a análise de dados educacionais algo dispendioso e pouco viável. Este trabalho apresenta um estudo do uso de ferramentas de visualização de dados, que de maneira integrada aos softwares educacionais poderiam auxiliar o professor a identificar problemas e avaliar o desenvolvimento tanto individual quanto global das suas turmas. Este artigo utiliza as ferramentas Ggobi e Hoffman, apresentando os resultados a partir de dados oriundos do software Geometry Tutor.

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A User Assistant for the Selection and Parameterization of the Visualizations in Visual Data Mining

Cited by 5 publications

References 8 publications

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

VizAssist: an interactive user assistant for visual data mining

Facilitando a Análise se Dados Educacionais através se Ferramentas se Visualização

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