Information Visualization and Visual Data Mining

Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication. Important stories live in our data and data visualization is a powerful means to discover and understand these stories, and then to present them to others. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special issue

Visual data mining: integrating machine learning with information visualization

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework

3D Visualization and virtual reality for visual data mining - a survey

Visual Data Mining (VDM) aims at an easier interpretation of data mining algorithm results through the use of visualization techniques. During the last decade, many techniques of information visualization have been proposed, allowing visualization of multidimensional data. Previously, ((Chi, 2000), (Herman et al., 2000)) attempted to classify VDM techniques . However, these taxonomies do not take into account some innovative techniques based on 3D visualization and virtual environments (VEs). In this paper, we propose an exhaustive survey of recent techniques for VDM. These different techniques are detailed, classified and compared according to the following criteria : graphical encoding, interaction techniques and applications. Moreover, they are presented in tables together with graphical illustrations

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

From visual data exploration to visual data mining: a survey.

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension

Fused visualization of complex information spaces

University of Technology, Sydney. Faculty of Information Technology.With the rapid growth of information analysis and data mining technologies, the massive data sets available for access have been merged and refined to manifold information, including raw data and all kinds of analytical results. Since data sets become increasingly complex, the current visual analytical techniques no longer satisfy the needs of exploring and analyzing data. This situation raises the challenges in the current state of information visualization: 1) Due to the complexity of information, sometimes it is unlikely to use a single visual metaphor to model the intricate information well in a single visualization. 2) Each existing visualization method has its own limitations in terms of satisfying domain specific requirements, when dealing with complex data sets. The proposed fused visualization methodology attempts to address the above issues by combining multiple existing visualization techniques in a single visualization. It takes the advantages and reduces the weaknesses of the existing methods. We have successfully applied this methodology to each stage of the proposed Analytical Information Visualization. In particular, three fused visualization techniques are developed to improve the quality of existing techniques. First, a fused visual metaphor that combines two visual metaphors in a single visualization allows users to navigate spatially referenced information across two different metaphors. Second, a fused layout algorithm that combines two graph drawing methods achieves the fast convergence in geometric layout for the force-directed layout algorithm; Third, a fused viewing technique that combines ID and 2D distortional visual viewing methods in one browser resolves the inefficient space utilization problem. Moreover, the fused layout algorithm has been evaluated against other existing force-directed layout algorithms. Two case studies that apply our techniques to an outbreak management system and an online bookstore respectively have been delivered

Mining aeronautical data by using visualized driven rules extraction approach

International audienceData Mining aims at researching relevant information from a huge volume of data. It can be automatic thanks to algorithms, or manual, for instance by using visual exploration tools. An algorithm finds an exhaustive set of patterns matching specific measures. But, depending on measures thresholds, the volume of extracted information can be greater than the volume of initial data. The second approach is Visual Data Mining which helps the specialist to focus on specific areas of data that may describe interesting patterns. However it is generally limited by the difficulty to tackle a great number of multi dimensional data. In this paper, we propose both methods, by combining the use of algorithms with manual visual data mining. From a scatter plot visualization, an algorithm generates association rules, depending on the visual variables assignments. Thus they have a direct effect on the construction of the found rules. Then we characterize the visualization with the extracted association rules in order to show the involvement of the data in the rules, and then which data can be used for predictions. We illustrate our method on two databases. The first describes one month French air traffic and the second stems from a FAA database about delays and cancellations causes

Visual Data Mining with Information Visualization

Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication. Important stories live in our data and data visualization is a powerful means to discover and understand these stories, and then to present them to others. In this paper, we propose a classification of information visualizationand visual data mining techniques which is based on the data type to be visualized, the visualization technique and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special issue

From Visualization to Association Rules : an automatic approach

International audienceThe main goal of Data Mining is the research of relevant information from a huge volume of data. It is generally achieved either by automatic algorithms or by the visual exploration of data. Thanks to algorithms, an exhaustive set of patterns matching specific measures can be found. But the volume of extracted information can be greater than the volume of initial data. Visual Data Mining allows the specialist to focus on a specific area of data that may describe interesting patterns. However, it is often limited by the difficulty to deal with a great number of multi dimensional data. In this paper, we propose to mix an automatic and a manual method, by driving the automatic extraction using a data scatter plot visualization. This visualization affects the number of rules found and their construction. We illustrate our method on two databases. The first describes one month French air traffic and the second stems from 2012 KDD Cup database