Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces

Chegini, Mohammad; Shao, Lin; Gregor, Robert J.; Lehmann, Dirk J.; Andrews, Keith; Schreck, Tobias

doi:10.1111/cgf.13404

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…Bernard et al (2018a) ran an experiment to show interactive visual labelling strategies can outperform pure active learning algorithms in terms of performance and accuracy. Later, Chegini et al (2019a) integrated interactive labelling into mVis, a tool built based on previous work by Shao et al (2017) and Chegini et al (2018). mVis provides visual analysis of high-dimensional data using multiple coordinated views, including similarity maps, SPLOM, and parallel coordinates.…”

Section: Related Workmentioning

confidence: 99%

Interactive visual labelling versus active learning: an experimental comparison

Chegini

Bernard

Cui

et al. 2020

Front Inform Technol Electron Eng

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Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised learning depends not only on the type of algorithm used but, importantly, also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually, relying on selections and annotations by expert analysts and is often a tedious and time-consuming process. Active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process. Interactive visual labelling techniques are a promising alternative, providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label. By putting the analyst in the loop, higher accuracy can be achieved in the resulting classifier. While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning, many aspects of these techniques are still largely unexplored. This paper presents a study conducted using the mVis tool to compare three interactive visualisations (similarity map, SPLOM with scatterplot, and parallel coordinates) with each other and with active learning for the purpose of labelling a multivariate dataset. The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy and that users subjectively prefer the similarity map over SPLOM with scatterplot and parallel coordinates for labelling. Users also employed different labelling strategies depending on the visualisation being used.

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Section: Related Workmentioning

confidence: 99%

Interactive visual labelling versus active learning: an experimental comparison

Chegini

Bernard

Cui

et al. 2020

Front Inform Technol Electron Eng

Self Cite

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“…Ahmed et al [AYMW11] use a qualitative color scheme in order to encode cluster groupings in all views of their visualization tool for steering mixed‐dimensional KD‐KMeans clustering. Color is used in almost every paper we examined [CSG*18, EASKC18, KS12, ML14, PSF17, XCH*16]. DeepCompare [MMD*19] uses opacity and size , which are the two second‐most occurring visual variables .…”

Section: In‐depth Categorization Of Trust Against Facets Of Interamentioning

confidence: 99%

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas

Martins

Jusufi

et al. 2020

Computer Graphics Forum

143

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Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

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“…Chegini et al [CSG∗18] developed a system supporting the visual exploration of patterns in large scatter‐plot matrices. Usually, the analysis space is huge, so to reduce users' effort, the system recommends suitable patterns for close‐up investigations.…”

Section: User Guidancementioning

confidence: 99%

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

Ceneda

Gschwandtner

Miksch

2019

Computer Graphics Forum

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Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system‐user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed‐initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human‐computer collaboration, and thus, promote a more effective visual data analysis.

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Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces

Cited by 10 publications

References 26 publications

Interactive visual labelling versus active learning: an experimental comparison

Interactive visual labelling versus active learning: an experimental comparison

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

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