1,063 research outputs found
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation
We present LDAExplore, a tool to visualize topic distributions in a given
document corpus that are generated using Topic Modeling methods. Latent
Dirichlet Allocation (LDA) is one of the basic methods that is predominantly
used to generate topics. One of the problems with methods like LDA is that
users who apply them may not understand the topics that are generated. Also,
users may find it difficult to search correlated topics and correlated
documents. LDAExplore, tries to alleviate these problems by visualizing topic
and word distributions generated from the document corpus and allowing the user
to interact with them. The system is designed for users, who have minimal
knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a
pilot study which uses the abstracts of 322 Information Visualization papers,
where every abstract is considered a document. The topics generated are then
explored by users. The results show that users are able to find correlated
documents and group them based on topics that are similar
Reducing Ambiguities in Line-based Density Plots by Image-space Colorization
Line-based density plots are used to reduce visual clutter in line charts
with a multitude of individual lines. However, these traditional density plots
are often perceived ambiguously, which obstructs the user's identification of
underlying trends in complex datasets. Thus, we propose a novel image space
coloring method for line-based density plots that enhances their
interpretability. Our method employs color not only to visually communicate
data density but also to highlight similar regions in the plot, allowing users
to identify and distinguish trends easily. We achieve this by performing
hierarchical clustering based on the lines passing through each region and
mapping the identified clusters to the hue circle using circular MDS.
Additionally, we propose a heuristic approach to assign each line to the most
probable cluster, enabling users to analyze density and individual lines. We
motivate our method by conducting a small-scale user study, demonstrating the
effectiveness of our method using synthetic and real-world datasets, and
providing an interactive online tool for generating colored line-based density
plots
Visualizing Spatio-Temporal data
The amount of spatio-temporal data produced everyday has sky rocketed in the recent years due to the commercial GPS systems and smart devices. Together with this, the need for tools and techniques to analyze this kind of data have also increased. A major task of spatio-temporal data analysis is to discover relationships and patterns among spatially and temporally scattered events. However, most of the existing visualization techniques implement a top-down approach i.e, they require prior knowledge of existing patterns. In this dissertation, I present my novel visualization technique called Storygraph which supports bottom-up discovery of patterns. Since Storygraph presents and integrated view, analysis of events can be done with losing either of time or spatial contexts. In addition, Storygraph can handle spatio-temporal uncertainty making it ideal for data being extracted from text. In the subsequent chapters, I demonstrate the versatility and the effectiveness of the Storygraph along with case studies from my published works. Finally, I also talk about edge bundling in Storygraph to enhance the aesthetics and improve the readability of Storygraph
DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling
The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift , a novel visual analysis method for creating and interacting with dimensional bundles . Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.acceptedVersio
Scalability considerations for multivariate graph visualization
Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations
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