We present in this paper a visual method to explore the properties of an image dataset and its internal representation learned by a convolutional neural network. We consider the inner characteristics extracted by the network just before the classification layers. We build a neighborhood graph from this vector space by connecting data together according to specific topological properties. We define typical examples of topological anomalies to be detected (isolated points, erroneous points, class boundaries). Then we propose a visualization of this graph highlighting this information and offering an overview of the graph (groups of data) as well as local details (fine topological properties). This visualization includes a representation of the images in order to let the user understand what can cause an error (errors during image acquisition, pre-processing or labeling, or errors due to the choice of the network or the learning parameters, etc.). We perform several tests with the VGG16 network on samples of standard datasets.

We consider in this paper a radial visualization called POIViz as a starting point to be improved with an optimization procedure. In our previous work, we studied POIViz and showed that it was able to represent multidimensional data in 2D within a few seconds, even for datasets with millions of records. We provided a simple heuristic to select the Points Of Interest (POIs), i.e., the 2D anchors that determine the layout of the data. In this paper, we extend POIViz to Gen-POIViz by proposing a genetic algorithm (GA) that can greatly optimize the quality of the visualization. The GA searches for a set of POIs that minimizes a cost function that is based on Kruskal's stress. Furthermore, Gen-POIViz can find relevant POIs with a small sample of the data only, and thus it can compute a projection of the complete data in a very short time. We provide comparative results with standard methods in data projection. Gen-POIViz obtains results with a quality that is between force-directed Multidimensional Scaling (MDS) and Principal Components Analysis (PCA). On larger datasets, we show the advantage of our method when it works on a data sample. It can be much faster than MDS, and it can be run with even larger datasets for which other methods fail.

We study in this paper the use of a 3D interface for OLAP. We analyze the state of the art in OLAP 3D visualizations. In a first step, we propose a new interface, called VR4OLAP, that combines the relative advantages of the studied methods. VR4OLAP can visualize 3 dimensions of an OLAP data cube and up to two measures. It represents the OLAP operators in the visualization with 3D widgets. It can use a 3D stereoscopic screen with a 3D mouse. In a second step, we perform a user study to evaluate this interface and to compare it with a standard cross-table. We evaluate the technical performances of the 3D interface. We conclude that, for the studied data and methods, 1) users are quite enthusiastic with a 3D representation, 2) the performances in 2D are equal or better than those obtained in 3D, 3) no advantage was found with the use of the 3D immersive setup.

In this paper, we propose a new method for the visual reorganization of online analytical processing (OLAP) cubes that aims at improving their visualization. Our method addresses dimensions with hierarchically organized members. It uses a genetic algorithm that reorganizes k-ary trees. Genetic operators perform permutations of subtrees to optimize a visual homogeneity function. We propose several ways to reorganize an OLAP cube depending on which set of members is selected for the reorganization: all of the members, only the displayed members, or the members at a given level (level by level approach). The results that are evaluated by using optimization criteria show that our algorithm has a reliable performance even when it is limited to 1 minute runs. Our algorithm was integrated in an interactive 3D interface for OLAP. A user study was conducted to evaluate our approach with users. The results highlight the usefulness of reorganization in two OLAP tasks.

We deal in this paper with the problem of automating the process of choosing an appropriate visualization and its parameters in the context of visual data mining (VDM). To solve this problem, we develop a user assistant that performs 2 steps: the system starts by suggesting to users different mappings between their data and possible visualizations. This is performed with a simple but generic heuristic that can be applied to any visualization. Then, the user selects a visualization among those proposed by our assistant, and he may further improve the parameters set that defines the mapping between the visual attributes and the data attributes. For this purpose, we use an interactive genetic algorithm (IGA), which allows users to visually evaluate and adjust the mappings. We present a user evaluation that confirms the interest of our system in two tasks.

In this paper we present a new incremental algorithm for building neighborhood graphs between data. It is inspired from the self-assembly behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants. Each artificial ant represents one data. The way ants move and build a graph depends on the similarity between the data. We have compared our results to those obtained by the relative neighborhood algorithm on several databases (either artificial or real), and we show that our method is competitive especially with respect to execution times.

We present an incremental algorithm for building a neighborhood graph from a set of documents. This algorithm is based on a population of artificial agents that imitate the way real ants build structures with self-assembly behaviors. We show that our method outperforms standard algorithms for building such neighborhood graphs (up to 2230 times faster on the tested databases with equal quality) and how the user may interactively explore the graph.

This paper presents a new bio-inspired algorithm (FClust) that dynamically creates and visualizes groups of data. This algorithm uses the concepts of a flock of agents that move together in a complex manner with simple local rules. Each agent represents one data. The agents move together in a 2D environment with the aim of creating homogeneous groups of data. These groups are visualized in real time, and help the domain expert to understand the underlying structure of the data set, like for example a realistic number of classes, clusters of similar data, isolated data. We also present several extensions of this algorithm, which reduce its computational cost, and make use of a 3D display. This algorithm is then tested on artificial and real-world data, and a heuristic algorithm is used to evaluate the relevance of the obtained partitioning.

The aim of web pages visualization is to present in a very informative and interactive way a set of web documents to the user in order to let him or her navigate through these documents. In the web context, this may correspond to several user's tasks: displaying the results of a search engine, or visualizing a graph of pages such as a hypertext or a surf map. In addition to web pages visualization, web pages clustering also greatly improves the amount of information presented to the user by highlighting the similarities between the documents [6]. In this paper we explore the use of a cellular automata (CA) to generate such maps of web pages.

We present a new bio-inspired algorithm that dynamically creates and visualizes groups of data. This algorithm uses the concepts of a flock of agents that move together in a complex manner with simple local rules. Each agent represents one data. The agents move with the aim of creating homogeneous groups of data that evolve together in a 2D environment. These created groups are visualized in real time and help the domain expert to understand the underlying class structure of the data set, like for example a realistic number of classes, clusters of similar data, isolated data, etc. We present several extensions of this algorithm and present results from artificial and real-world data.