Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a Maximum Entropy distribution of the data, after which the system provides the user data projections in which the data and the Maximum Entropy distribution differ the most, hence showing the user aspects of the data that are maximally informative given the user's current knowledge. We provide an open source EDA system with tailored interactive visualizations to demonstrate these concepts. We study the performance of the system and present use cases on both synthetic and real data. We find that the model and the prototype system allow the user to learn information efficiently from various data sources and the system works sufficiently fast in practice. We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.
The system presented here can be also considered to be an instance of visually controllable data mining @cite_28 , where the objective is to implement advanced data analysis methods understandable and efficiently controllable by the user. Our approach satisfies the properties of a visually controllable data mining method (see @cite_28 , Sec. II B): (VC1) the data and model space are presented visually, (VC2) there are intuitive visual interactions allowing the user to modify the model space, and (VC3) the method is fast enough for visual interaction.
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A large number of data mining methods are, as such, not applicable to fast, intuitive, and interactive use. Thus, there is a need for visually controllable data mining methods. Such methods should comply with three major requirements: their model structure can be represented visually, they can be controlled using visual, interaction, and they should be fast enough for visual interaction. We, define a framework for using data mining methods in, interactive visualization. These data mining methods are called, visually controllable'' and combine data mining with visualization, and user-interaction, bridging the gap between data mining and, visual analytics. Our main objective is to define the interactive, visualization scenario and the requirements for visually, controllable data mining. Basic data mining algorithms are reviewed, and it is demonstrated how they can be controlled visually. We also discuss how existing visual analytics tools fit to the proposed framework., From a data mining perspective, this work creates a reference framework for, designing and evaluating visually controllable algorithms and visual analytics systems. Interactive visualization systems for exploring and manipulating high-dimensional feature spaces have experienced a substantial progress in the last few years. State-of-art methods rely on solid mathematical and computational foundations that enable sophisticated and flexible interactive tools. Current methods are even capable of modifying data attributes during interaction, highlighting regions of potential interest in the feature space, and building visualizations that bring out the relevance of attributes. However, those methodologies rely on complex and non-intuitive interfaces that hamper the free handling of the feature spaces. Moreover, visualizing how neighborhood structures are affected during the space manipulation is also an issue for existing methods. This paper presents a novel visualization-assisted methodology for interacting and transforming data attributes embedded in feature spaces. The proposed approach relies on a combination of multidimensional projections and local transformations to provide an interactive mechanism for modifying attributes. Besides enabling a simple and intuitive visual layout, our approach allows the user to easily observe the changes in neighborhood structures during interaction. The usefulness of our methodology is shown in an application geared to image retrieval. Visual analytics emphasizes sensemaking of large, complex datasets through interactively exploring visualizations generated by statistical models. For example, dimensionality reduction methods use various similarity metrics to visualize textual document collections in a spatial metaphor, where similarities between documents are approximately represented through their relative spatial distances to each other in a 2D layout. This metaphor is designed to mimic analysts' mental models of the document collection and support their analytic processes, such as clustering similar documents together. However, in current methods, users must interact with such visualizations using controls external to the visual metaphor, such as sliders, menus, or text fields, to directly control underlying model parameters that they do not understand and that do not relate to their analytic process occurring within the visual metaphor. In this paper, we present the opportunity for a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. Further, we demonstrate how semantic interactions can be implemented using machine learning techniques in a visual analytic tool, called ForceSPIRE, for interactive analysis of textual data within a spatial visualization. Analysts can express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user's feedback into account. We study in this work how a user can be guided to find a relevant visualization in the context of visual data mining. We present a state of the art on the user assistance in visual and interactive methods. We propose a user assistant called VizAssist, which aims at improving the existing approaches along three directions: it uses simpler computational models of the visualizations and the visual perception guidelines, in order to facilitate the integration of new visualizations and the definition of a mapping heuristic. VizAssist allows the user to provide feedback in a visual and interactive way, with the aim of improving the data to visualization mapping. This step is performed with an interactive genetic algorithm. Finally, VizAssist aims at proposing a free on-line tool (www.vizassist.fr) that respects the privacy of the user data. This assistant can be viewed as a global interface between the user and some of the many visualizations that are implemented with D3js. To date, work in microarrays, sequenced genomes and bioinformatics has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts. Effective data visualization is a key part of the discovery process in the era of “big data”. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding. Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis. However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle. How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces? A better understanding of how we can perceive and interact with multidimensional information poses some deep questions in the field of cognition technology and human-computer interaction. To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware. These potentially powerful and innovative tools for multi-dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space. Immersion provides benefits beyond the traditional “desktop” visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data. 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. Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Due to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or self-organizing map, or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations, or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on a trajectory clustering problem, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results. Abstract Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed. The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. In many application fields, data analysts have to deal with datasets that contain many expressions per item. The effective analysis of such multivariate datasets is dependent on the user's ability to understand both the intrinsic dimensionality of the dataset as well as the distribution of the dependent values with respect to the dimensions. In this paper, we propose a visualization model that enables the joint interactive visual analysis of multivariate datasets with respect to their dimensions as well as with respect to the actual data values. We describe a dual setting of visualization and interaction in items space and in dimensions space. The visualization of items is linked to the visualization of dimensions with brushing and focus+context visualization. With this approach, the user is able to jointly study the structure of the dimensions space as well as the distribution of data items with respect to the dimensions. Even though the proposed visualization model is general, we demonstrate its application in the context of a DNA microarray data analysis. The visualization and analysis of dynamic networks have become increasingly important in several fields, for instance sociology or economics. The dynamic and multi-relational nature of this data poses the challenge of understanding both its topological structure and how it changes over time. In this paper we propose a visual analytics approach for analyzing dynamic networks that integrates: a dynamic layout with user-controlled trade-off between stability and consistency; three temporal views based on different combinations of node-link diagrams (layer superimposition, layer juxtaposition, and two-and-a-half-dimensional view); the visualization of social network analysis metrics; and specific interaction techniques for tracking node trajectories and node connectivity over time. This integration of visual, interactive, and automatic methods supports the multi-faceted analysis of dynamically changing networks. Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method. Exploration of graph structures is an important topic in data mining and data visualization. This work presents a novel technique for visualizing neighbourhood and cluster relationships in graphs; we also show how this methodology can be used within the setting of a recommendation system. Our technique works by projecting the original object distances onto two dimensions while carefully retaining the 'backbone' of important distances. Cluster information is also overlayed on the same projected space. A significant advantage of our approach is that it can accommodate both metric and non-metric distance functions. Our methodology is applied to a visual recommender system for movies to allow easy exploration of the actor-movie bipartite graph. The work offers intuitive movie recommendations based on a selected pivot movie and allows the interactive discovery of related movies based on both textual and semantic features. For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined ‘Big Data’, massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data. Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. 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 section. Atrial arrhythmias like atrial fibrillation and atrial flutter are a major health challenge in developed countries. Radiofrequency ablation performed via intracardiac catheters is a curative therapy for these reentrant arrhythmias. However, the optimal location of ablation lesions is not straightforward to determine, particularly for complex activation patterns. Thus, a clinical need for tools to intuitively visualize complex activation patterns and to provide a platform to evaluate different ablation strategies in dry runs is apparent. Here, we present a virtual reality system that allows to interactively simulate atrial excitation propagation and place ablation lesions. Our software builds on the IMHOTEP framework for the Unity3D engine and implements a multithreaded model-view-controller design pattern. Excitation propagation is computed using a fast marching approach considering refractoriness. Interactive rewind and playback is supported through a combination of the flyweight pattern for simulation data with complete snapshots for key frames. The system was evaluated in a user study using the HTC Vive™ headset including two controllers. For high fidelity virtual reality interaction, a minimum frame rate of 60 per second is required. In a biatrial anatomical model comprising 36,059 nodes (Figure 1), even complex activation patterns with multiple wavefronts could be simulated and rendered down to 2× slow motion (1 sec activation sequence displayed during 2 sec wall time) on a desktop machine. Results of the user study suggest added value regarding the comprehension of arrhythmias and ablation options and very good intuitiveness of the user interface requiring almost no teach-in. The virtual reality tool is ready to be used for educational purposes and prepared to import personalized models supporting diagnosis and therapy planning for atrial arrhythmias in the future. Thanks to advances in non-invasive technologies such as functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), highly-detailed maps of brain structure and function can now be collected. In this context, brain connectomics have emerged as a fast growing field that aims at understanding these comprehensive maps of brain connectivity using sophisticated computational models. In this paper we present BRAINtrinsic, an innovative web-based 3D visual analytics tool that allows users to intuitively and iteratively interact with connectome data. Moreover, BRAINtrinsic implements a novel visualization platform that reconstructs connectomes’ intrinsic geometry, i.e., the topological space as informed by brain connectivity, via dimensionality reduction. BRAINtrinsic is implemented with virtual reality in mind and is fully compatible with the Oculus Rift technology. Last, we demonstrate its effectiveness through a series of case studies involving both structural and resting-state MR imaging data. Improvise is a fully-implemented system in which users build and browse multiview visualizations interactively using a simple shared-object coordination mechanism coupled with a flexible, expression-based visual abstraction language. By coupling visual abstraction with coordination, users gain precise control over how navigation and selection in the visualization affects the appearance of data in individual views. As a result, it is practical to build visualizations with more views and richer coordination in Improvise than in other visualization systems. Building and browsing activities are integrated in a single, live user interface that lets users alter visualizations quickly and incrementally during data exploration We explore the use of customizable tangible remote controllers for interacting with wall-size displays. Such controllers are especially suited to visual exploration tasks where users need to move to see details of complex visualizations. In addition, we conducted a controlled user study suggesting that tangibles make it easier for users to focus on the visual display while they interact. We explain how to build such controllers using off-the-shelf touch tablets and describe a sample application that supports multiple dynamic queries.