Using visual data mining in highway traffic safety analysis and decision making

An ongoing, two-fold challenge involves extracting useful information from the massive amounts of highway crash data and explaining complicated statistical models to inform the public about highway safety. Highway safety is critical to the trucking industry and highway funding policy. One method to analyze complex data is through the application of visual data mining tools. In this paper, we address the following three questions: a) what existing data visualization tools can assist with highway safety theory development and in policy-making?; b) can visual data mining uncover unknown relationships to inform the development of theory or practice? and c) can a data visualization toolkit be developed to assist the stakeholders in understanding the impact of publicpolicy on transportation safety? To address these questions, we developed a visual data mining toolkit that allows for understanding safety datasets and evaluating the effectiveness of safety policies

Visualizing Incongruity: Visual Data Mining Strategies for Modeling Humor in Text

The goal of this project is to investigate the use of visual data mining to model verbal humor. We explored various means of text visualization to identify key featrues of garden path jokes as compared with non jokes. With garden path jokes one interpretation is established in the setup but new information indicating some alternative interpretation triggers some resolution process leading to a new interpretation. For this project we visualize text in three novel ways, assisted by some web mining to build an informal ontology, that allow us to see the differences between garden path jokes and non jokes of similar form. We used the results of the visualizations to build a rule based model which was then compared with models from tradtitional data mining toi show the use of visual data mining. Additional experiments with other forms of incongruity including visualization of ’shilling’ or the introduction of false reviews into a product review set. The results are very similar to that of garden path jokes and start to show us there is a shape to incongruity. Overall this project shows as that the proposed methodologies and tools offer a new approach to testing and generating hypotheses related to theories of humor as well as other phenomena involving opposition, incongruities, and shifts in classification

How to evaluate a subspace visual projection in interactive visual systems? A position paper

International audienceThis paper presents a position paper on subspace projection evaluation methods in interactive visual systems. We focus on how to evaluate real information rendered through the visual data projection for the mining of high dimensional data sets. To do this, we investigate automatic techniques that select the best visual projection and we discuss how they evaluate the projections to help the user before interactivity. When we deal with high dimensional data sets, the number of potential projections exceeds the limit of human interpretation. To find the optimal subspace representation, there are two possibilities, the first one is to find the optimal subspace which reproduces what really exists in the original data: getting the existing clusters and/or outliers in the projection. The second possibility consists in researching subspaces according to the knowledge discovery process: discovering novel, but meaningful information, such as clusters and/or outliers from the projection. The problem is that visual projection cannot be in adequation with the subspaces. In some cases, the visual projection can show some things that do not really exist in the original data space (which can be considered as an artifact). The mapping between the visual structure and the real data structure is as important as the efficiency and accuracy of the visualization. We examine and discuss the literature of Information visualization, Visual analytic, High dimensional data visualization, and interactive data mining and machine learning communities, on how to evaluate the faithfulness of the visual projection information

Trading Consequences: A Case Study of Combining Text Mining and Visualization to Facilitate Document Exploration

Large-scale digitization efforts and the availability of computational methods, including text mining and information visualization, have enabled new approaches to historical research. However, we lack case studies of how these methods can be applied in practice and what their potential impact may be. Trading Consequences is an interdisciplinary research project between environmental historians, computational linguists and visualization specialists. It combines text mining and information visualization alongside traditional research methods in environmental history to explore commodity trade in the nineteenth century from a global perspective. Along with a unique data corpus, this project developed three visual interfaces to enable the exploration and analysis of four historical document collections, consisting of approximately 200,000 documents and 11 million pages related to commodity trading. In this paper we discuss the potential and limitations of our approach based on feedback from historians we elicited over the course of this project. Informing the design of such tools in the larger context of digital humanities projects, our findings show that visualization-based interfaces are a valuable starting point to large-scale explorations in historical research. Besides providing multiple visual perspectives on the document collection to highlight general patterns, it is important to provide a context in which these patterns occur and offer analytical tools for more in-depth investigations.PostprintPeer reviewe

Inventing Discovery Tools: Combining Information Visualization with Data Mining (2001)

The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. This paper discusses two issues that influence design of discovery tools: statistical algorithms vs. visual data presentation, and hypothesis testing vs. exploratory data analysis. I claim that a combined approach could lead to novel discovery tools that preserve user control, enable more effective exploration, and promote responsibility

Visual Analytics of High-dimensional Data Sets: A Hyperspectral Imagery Test Case

Visualization and interpretation of big data poses new and unique challenges. As engineering students enter the work force, many will be tasked with analyzing increasingly large and complex data sets with which they have little experience. This paper presents simple heat map and multi-line plotting techniques used to select critical spectral attributes produced from data mining a hyperspectral satellite image for bathymetry mapping. Additionally, good graphic design practices regarding color choice and reducing visual distraction are suggested in order to more quickly and clearly communicate information to an audience. These techniques can be applied to all types of data visualization as an effective way of communicating data

Clear Visual Separation of Temporal Event Sequences

Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be difficult to tell whether it is appropriate to combine multiple events into one or utilize additional information about event attributes. Existing approaches often make use of frequent sequential patterns extracted from the dataset, however, these patterns are limited in terms of interpretability and utility. In addition, it is difficult to assess the role of absolute and relative time when using pattern mining techniques. In this paper, we present methods that addresses these challenges by automatically learning composite events which enables better aggregation of multiple event sequences. By leveraging event sequence outcomes, we present appropriate linked visualizations that allow domain experts to identify critical flows, to assess validity and to understand the role of time. Furthermore, we explore information gain and visual complexity metrics to identify the most relevant visual patterns. We compare composite event learning with two approaches for extracting event patterns using real world company event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data Science (VDS), 201

Information Visualization (iV): Notes about the 9th IV ’05 International Conference, London, England

This review tells about the International Conference on Information Visualization that is held annually in London, England. Themes selected from the Conference Proceedings are focused on theoretical concepts, semantic approach to visualization, digital art, and involve 2D, 3D, interactive and virtual reality tools and applications. The focal point of the iV 05 Conference was the progress in information and knowledge visualization, visual data mining, multimodal interfaces, multimedia, web graphics, graph theory application, augmented and virtual reality, semantic web visualization, HCI, digital art, among many other areas such as information visualization in geology, medicine, industry and education