Improving Visualization of Large Hierarchical Clustering

Bisson, Gilles; Blanch, Renaud

doi:10.1109/iv.2012.45

Cited by 12 publications

(14 citation statements)

References 38 publications

(28 reference statements)

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“…They have e.g., been adopted by biologists to show genome sequence clusters [13]. Bisson and Blanch [9] propose stacked trees for showing cluster hierarchy and content in a single visualization for cluster comparison. Seo and Shneiderman [36] present an example of a dendrogram-based view coupled with a heatmap showing the details of the cluster.…”

Section: Visual Analysis Of High-dimensional Aggregated Datamentioning

confidence: 99%

MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

Bernard

Wilhelm

Krüger

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

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Section: Visual Analysis Of High-dimensional Aggregated Datamentioning

confidence: 99%

MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

Bernard

Wilhelm

Krüger

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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“…Such hybrid visualizations have been used to display large dendrograms. In Stacked Trees [3], the classical dendrogram visualization is used for the largest clusters, but above an homogeneity threshold the visualization switches to a stack of leaves rather than going on with the tree representation. This saves space at the expense of loosing structural information.…”

Section: Hybrid Visualizationsmentioning

confidence: 99%

Dendrogramix: A hybrid tree-matrix visualization technique to support interactive exploration of dendrograms

Blanch

Dautriche

Bisson

2015

2015 IEEE Pacific Visualization Symposium (PacificVis)

Self Cite

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Clustering is often a first step when trying to make sense of a large data set. A wide family of cluster analysis algorithms, namely hierarchical clustering algorithms, does not provide a partition of the data set but a hierarchy of clusters organized in a binary tree, known as a dendrogram. The dendrogram has a classical nodelink representation used by experts for various tasks like: to decide which subtrees are actual clusters (e.g., by cutting the dendrogram at a given depth); to give those clusters a name by inspecting their content; etc. We present Dendrogramix, a hybrid tree-matrix interactive visualization of dendrograms that superimposes the relationship between individual objects on to the hierarchy of clusters. Dendrogramix enables users to do tasks which involve both clusters and individual objects that are impracticable with the classical representation, like: to explain why a particular objects belongs to a particular cluster; to elicit and understand uncommon patterns (e.g., objects that could have been classified in a totally different cluster); etc. Those sensemaking tasks are supported by a consistent set of interaction techniques that facilitates the exploration of large clustering results.

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“…As large data sets become more and more common, with the size over 1K, it has been clear that most of the current visualization approaches lose their effectiveness due to they have no ability to visualize and manage the large number of data points simultaneously. In such scenario, clustering is considered a suitable method for understanding and exploring large data [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, when dealing practically with a large size dendrogram, it becomes difficult since the number of nodes grows exponentially with the depth of the tree and makes users lose the overview of the whole dataset. To deal with a large size dendrogram, many approaches have been suggested [6], [10], [12]. However, these methods are either display at one time only a sub-part of the structure [10], [12], or display whole dendrogram but rely on other clustering technique [6].…”

Section: Introductionmentioning

confidence: 99%

Multiple-Scale Visualization of Large Data Based on Hierarchical Clustering

Nguyen¹,

Olivetti²,

Avesani³

2014

IJCEE

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Abstract-Nowadays, the large datasets become more and more common. However, traditional visualization techniques, which although allow to visually analyze and explore data, can not scale well with the large one. This restrains the ability of detecting, recognizing and classifying phenomena of interest, such as patterns, clusters, trends, etc. This Paper proposes a method for interactive multi-resolution visualization to overcome the problem of traditional visualization techniques when working with a large dataset via hierarchical clustering. Based on hierarchical clustering, users can not only examine the dataset at different levels of detail, but also can explore many regions of interest. The basic idea underlying this method is to choose multiple scales from the hierarchical tree for representing the data at different levels of abstraction, which creates an easy environment for interactive exploration without re-run the clustering algorithm. Moreover, we also define a criterion for evaluating the multiple scale representation based on the concept of split factor. An experiment of applying the proposed method into the task of interactive visualization in a clinical case study of the large dMRI (diffusion magnetic resonance imaging) data is also carried out. The results show that our proposed method efficiently provides a friendly tool for visualization the large data.

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Improving Visualization of Large Hierarchical Clustering

Cited by 12 publications

References 38 publications

MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

Dendrogramix: A hybrid tree-matrix visualization technique to support interactive exploration of dendrograms

Multiple-Scale Visualization of Large Data Based on Hierarchical Clustering

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