Analysis of high dimensional data is a common task. Often, small multiples are used to visualize 1 or 2 dimensions at a time, such as in a scatterplot matrix. Associating data points between different views can be difficult though, as the points are not fixed. Other times, dimensional reduction techniques are employed to summarize the whole dataset in one image, but individual dimensions are lost in this view. In this paper, we present a means of augmenting a dimensional reduction plot with isocontours to reintroduce the original dimensions. By applying this to each dimension in the original data, we create multiple views where the points are consistent, which facilitates their comparison. Our approach employs a combination of a novel, graph-based projection technique with a GPU accelerated implementation of moving least squares to interpolate space between the points. We also present evaluations of this approach both with a case study and with a user study.
Many data projection and representation techniques can result in a high degree of overplot, where many data points map to the same area of the screen. Data reduction techniques, such as sampling @cite_44 @cite_38 or clustering @cite_33 @cite_8 , can be used to address the issue of overplot by creating an abstracted overview, but in doing so, they do not show the entire dataset. In order to explore the entire dataset from such an overview, semantic zooming such as fisheye lensing @cite_21 with user interaction is necessary. Several works @cite_27 @cite_10 @cite_13 @cite_16 employ such fisheye lensing. Many of them do so purely geometrically without the need for semantic abstraction. However, they do not account for points that are precisely collocated. @cite_6 address this issue by displacing overplotted points to the nearest free pixel. While this method does allow for all points to be drawn with minimum overplot, there is no visual indication of this displacement having occurred. Also, all of these methods use rectilinear distortion, which substantially limits their flexibility.
@cite_13 @cite_38 @cite_33 @cite_8 @cite_21 @cite_6 @cite_44 @cite_27 @cite_16 @cite_10
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Previous work has demonstrated the use of random sampling in visualising large data sets and the practicality of a sampling lens in enabling focus+context viewing. Autosampling was proposed as a mechanism to maintain constant density within the lens without user intervention. However, this requires rapid calculation of density or clutter. This paper defines clutter in terms of the occlusion of plotted points and evaluates three possible occlusion metrics that can be used with parallel coordinate plots. An empirical study showed the relationship between these metrics was independent of location and could be explained with a surprisingly simple probabilistic model. Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques. Our ability to accumulate large, complex (multivariate) data sets has far exceeded our ability to effectively process them in searching for patterns, anomalies and other interesting features. Conventional multivariate visualization techniques generally do not scale well with respect to the size of the data set. The focus of this paper is on the interactive visualization of large multivariate data sets based on a number of novel extensions to the parallel coordinates display technique. We develop a multi-resolution view of the data via hierarchical clustering, and use a variation of parallel coordinates to convey aggregation information for the resulting clusters. Users can then navigate the resulting structure until the desired focus region and level of detail is reached, using our suite of navigational and filtering tools. We describe the design and implementation of our hierarchical parallel coordinates system which is based on extending the XmdvTool system. Lastly, we show examples of the tools and techniques applied to large (hundreds of thousands of records) multivariate data sets. Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated regions, in a multi-dimensional dataset. Prior work does not adequately address the problem of large datasets and minimization of I O costs.This paper presents a data clustering method named BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and dynamically clusters incoming multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i.e., available memory and time constraints). BIRCH can typically find a good clustering with a single scan of the data, and improve the quality further with a few additional scans. BIRCH is also the first clustering algorithm proposed in the database area to handle "noise" (data points that are not part of the underlying pattern) effectively.We evaluate BIRCH's time space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a performance comparisons of BIRCH versus CLARANS, a clustering method proposed recently for large datasets, and show that BIRCH is consistently superior. Scatter Plots are one of the most powerful and most widely used techniques for visual data exploration. A well-known problem is that scatter plots often have a high degree of overlap, which may occlude a significant portion of the data values shown. In this paper, we propose the generalized scatter plot technique, which allows an overlap-free representation of large data sets to fit entirely into the display. The basic idea is to allow the analyst to optimize the degree of overlap and distortion to generate the bestpossible view. To allow an effective usage, we provide the capability to zoom smoothly between the traditional and our generalized scatter plots. We identify an optimization function that takes overlap and distortion of the visualization into acccount. We evaluate the generalized scatter plots according to this optimization function, and show that there usually exists an optimal compromise between overlap and distortion. Our generalized scatter plots have been applied successfully to a number of real-world IT services applications, such as server performance monitoring, telephone service usage analysis and financial data, demonstrating the benefits of the generalized scatter plots over traditional ones. The problem of visualizing huge amounts of data is well known in information visualization. Dealing with a large number of items forces almost any kind of Infovis technique to reveal its limits in terms of expressivity and scalability. In this paper we focus on 2D scatter plots, proposing a 'feature preservation' approach, based on the idea of modeling the visualization in a virtual space in order to analyze its features (e.g., absolute density, relative density, etc.). In this way we provide a formal framework to measure the visual overlapping, obtaining precise quality metrics about the visualization degradation and devising automatic sampling strategies able to improve the overall image quality. Metrics and algorithms have been improved through suitable user studies. Larger, higher resolution displays can be used to increase the scalability of information visualizations. But just how much can scalability increase using larger displays before hitting human perceptual or cognitive limits? Are the same visualization techniques that are good on a single monitor also the techniques that are best when they are scaled up using large, high-resolution displays? To answer these questions we performed a controlled experiment on user performance time, accuracy, and subjective workload when scaling up data quantity with different space-time-attribute visualizations using a large, tiled display. Twelve college students used small multiples, embedded bar matrices, and embedded time-series graphs either on a 2 megapixel (Mp) display or with data scaled up using a 32 Mp tiled display. Participants performed various overview and detail tasks on geospatially-referenced multidimensional time-series data. Results showed that current designs are perceptually scalable because they result in a decrease in task completion time when normalized per number of data attributes along with no decrease in accuracy. It appears that, for the visualizations selected for this study, the relative comparison between designs is generally consistent between display sizes. However, results also suggest that encoding is more important on a smaller display while spatial grouping is more important on a larger display. Some suggestions for designers are provided based on our experience designing visualizations for large displays This paper introduces a touch-sensitive two-dimensional scatter plot visualization, to explore and analyze movie data. The design focuses on the ability to work co-located with several users. These are able to create several focus regions through distortion techniques triggered by multi touch gestures. Furthermore, the introduced visualization is an example how promising concepts from InfoVis research can be transferred onto multi touch tables in order to offer more natural interaction. Information visualisation systems frequently have to deal with large amounts of data and this often leads to saturated areas in the display with considerable overplotting. This paper introduces the Sampling Lens, a novel tool that utilises random sampling to reduce the clutter within a moveable region, thus allowing the user to uncover any potentially interesting patterns and trends in the data while still being able to view the sample in context. We demonstrate the versatility of the tool by adding sampling lenses to scatter and parallel co-ordinate visualisations. We also consider some implementation issues and present initial user evaluation results.