Temporal MDS Plots for Analysis of Multivariate Data

Jäckle, Dominik; Fischer, Fabian; Schreck, Tobias; Keim, Daniel A.

doi:10.1109/tvcg.2015.2467553

Cited by 71 publications

(40 citation statements)

References 30 publications

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“…Furthermore, we identified various preprocessing configurations or parameters that can be adjusted by the analyst. An example is Jäckle et al's temporal MDS plot technique [29], where a parameter sets the size of a sliding window. The resulting slices are taken as input for subsequent DR by one-dimensional MDS.…”

Section: Seven Guiding Scenarios For Dr Interactionmentioning

confidence: 99%

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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203

139

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Abstract-Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

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Section: Seven Guiding Scenarios For Dr Interactionmentioning

confidence: 99%

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

Self Cite

203

139

View full text Add to dashboard Cite

show abstract

“…Methods based on variance such as PCA and LDA are ill-defined as the concept of a mean value is undefined for nominal variables. Distance-based methods, such as MDS, can also be used to visualize heterogeneous data as exemplified by Jäckle et al [JFSK16]. These methods depend on a distance metric between points usually in terms of (dis)similarity where categorical [dSZ15] and numerical distances are computed separately and then aggregated.…”

Section: Mixed Data Visualizationmentioning

confidence: 99%

Hinted Star Coordinates for Mixed Data

Matute

Linsen

2019

Computer Graphics Forum

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Mixed data sets containing numerical and categorical attributes are nowadays ubiquitous. Converting them to one attribute type may lead to a loss of information. We present an approach for handling numerical and categorical attributes in a holistic view. For data sets with many attributes, dimensionality reduction (DR) methods can help to generate visual representations involving all attributes. While automatic DR for mixed data sets is possible using weighted combinations, the impact of each attribute on the resulting projection is difficult to measure. Interactive support allows the user to understand the impact of data dimensions in the formation of patterns. Star Coordinates is a well‐known interactive linear DR technique for multi‐dimensional numerical data sets. We propose to extend Star Coordinates and its initial configuration schemes to mixed data sets. In conjunction with analysing numerical attributes, our extension allows for exploring the impact of categorical dimensions and individual categories on the structure of the entire data set. The main challenge when interacting with Star Coordinates is typically to find a good configuration of the attribute axes. We propose a guided mixed data analysis based on maximizing projection quality measures by the use of recommended transformations, named hints, in order to find a proper configuration of the attribute axes.

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“…1) Sliding window approach: Given a continuous time series data Q, the sliding window technique running along the time axis depends on two significant parameters which are window size W and stride (offset) S. Sliding Window is also called a brute force or one-pass algorithm [25] and has been used in many time series works [12], [13], [15], [29]. It is an appropriate way to deal with temporal data because it sequentially processes the raw data keeping into account its temporal behavior.…”

Section: A Preprocessingmentioning

confidence: 99%

Towards Visual Exploration of Large Temporal Datasets

Ali

Jones

Xie

et al. 2018

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)

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We address the problem of visualizing and interacting with large multi-dimensional time-series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.

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Temporal MDS Plots for Analysis of Multivariate Data

Cited by 71 publications

References 30 publications

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Hinted Star Coordinates for Mixed Data

Towards Visual Exploration of Large Temporal Datasets

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