Visual Data Mining

Network visualization tools offer features enabling a variety of analyses to satisfy diverse requirements. Considering complexity and diversity of data and tasks, there is no single best layout, no single best file format or visualization tool: one size does not fit all. One way to cope with these dynamics is to support multiple scenarios and workflows. NAViGaTOR (Network Analysis, Visualization

Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally , we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.

Deep-sea research relies strongly on the use of highresolution
cameras which generate large quantities of footage.
The material can currently, however, often not be used to its full
potential as the analysis is time- and labour-intensive and requires
the input of many different taxonomic experts. Here, we present a
system which enables the collaboration of experts from various
places and the application of machine-vision tools on footage
from the Arctic deep-sea observatory, HAUSGARTEN. Biigle
(Bielefeld Image Graphical Labeller and Explorer) is a Web 2.0
based platform containing easily uploaded images that can be
accessed by collaborating scientists. Since Biigle is realised as a
rich internet application, there is no need for the local installation
of complex software packages. The scientists can use a standard
web browser to access the image database and immediately
explore or label images. They have instant access to the data
submitted by other scientists and are directly involved in the
emerging community. Biigle also offers an application interface
for machine-vision components aiming at the automated analysis
of seafloor images. As a first module, a laser point detection allows
for an automated calibration of the area covered by the camera
which is vital to derive faunal density estimates. The laser points
were detected in all but eight of 1883 images tested in total. The
combination of human expert labels and machine-vision results
can be exported into spreadsheets offering a well-established
standard for further data analyses. Biigle can be accessed at
http://www.biigle.de with the username and the password “test”
for testing purposes.

We deal in this paper with the problem of automating the process of choosing a visualization and its parameters in visual data mining. To solve this problem, we have developed a user assistant that performs 2 steps: the system starts by suggesting to users different matchings between their database and the possible visualizations. These matchings are generated by using a knowledge-based heuristic. Then, in order to refine the different parameter settings suggested by the system, we use an interactive genetic algorithm which allows users to visually evaluate and adjust these parameters. We present a user evaluation that confirms the interest of our system in two tasks.

Objective: The paper addresses the problem of finding visual patterns in histology image collections. In particular, it proposes a method for correlating basic visual patterns with high-level concepts combining an appropriate image collection representation with state-of-the-art machine learning techniques.
Methodology: The proposed method starts by representing the visual content of the collection using a bag-of-features strategy. Then, two main visual mining tasks are performed: finding associations between visual-patterns and high-level concepts, and performing automatic image annotation. Associations are found using minimum-redundancy-maximum-relevance feature selection and co-clustering analysis. Annotation is done by applying a support-vector-machine classifier. Additionally, the proposed method includes an interpretation mechanism that associates concept annotations with corresponding image regions.
The method was evaluated in two data sets: one comprising histology images from the different four fundamental tissues, and the other composed of histopathology images used for cancer diagnosis. Different visual-word representations and codebook sizes were tested. The performance in both concept association and image annotation tasks was qualitatively and quantitatively evaluated.
Results: The results show that the method is able to find highly discriminative visual features and to associate them to high-level concepts. In the annotation task the method showed a competitive performance: an increase of 21\% in f-measure with respect to the baseline in the histopathology data set, and an increase of 47\% in the histology data set.
Conclusions: The experimental evidence suggests that the bag-of-features representation is a good alternative to represent visual content in histology images. The proposed method exploits this representation to perform visual pattern mining from a wider perspective where the focus is the image collection as a whole, rather than individual images.

On our attempt to handle adequately the age of the data glut, exploring and analyzing the vast volumes of data is becoming increasingly challenging, as never before in history has data been generated at such high volumes as it is today. In the context of geo-spatial data, large spatial data sets occur naturally when accumulating many samples or readings of phenomena in the real world. Visualization on the other hand, could be the link between the two most powerful information-processing systems: humans and the modern computer. Although the visual senses for humans have a unique status, offering a very broadband channel for information flow, they do also have critical limitations. In particular, we are quite limited in our ability to handle scale and are easily overwhelmed by the volumes of data that are now routinely collected. Data mining, as an automated knowledge extraction technique, could complement human capabilities. Combining these approaches for knowledge discovery is clear...

Visual data mining techniques have experienced a growing interest for processing and interpretation of the large amounts of multidimensional data available in current industrial processes. One of the approaches to visualize data is based on self-organizing maps ( ...

To ensure the normal operation of a large computer network sys- tem, the common practice is to constantly collect system logs and analyze the network activities for detecting anomalies. Most of the analysis methods in use today are highly automated due to the enor- mous size of the collected data. Conventional automated methods are largely based on statistical modeling, and

This paper describes a decade's worth of evolution of integrating software to support exploratory spatial data analysis (ESDA) where there are multiple measured attributes. The multivariate graph- ics tools we use are XGobi, and more recently, GGobi. The paper is divided into two parts. In the first part, we review early experiments in software linking for ESDA, which used XGobi,

Abstract. Recent advances in technology have made it possible to use 3-D Virtual Reality for Visual Data Mining. This paper presents a modular system architecture with a series of tools for explorative analysis of large data sets in Virtual Reality. A 3-D Scatter Plot tool is extended to become an ”Object Property Space”, where data records are visualized as objects with as many statistical variables as possible represented as object properties like shape, color, etc. A working hypothesis is that the free and real-time navigation of the observer in the immersive virtual space will support the chances of finding interesting data structures and relationships. The system is now ready to be used for experiments to validate the hypothesis.

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bioinformatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional cont...

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone ...

BACKGROUND: Survey data are increasingly abundant from many international projects and national statistics. They are generally comprehensive and cover local, regional as well as national levels census in many domains including health, demography, human development, and economy. These surveys result in several hundred indicators. Geographical analysis of such large amount of data is often a difficult task and searching for patterns is particularly a difficult challenge. Geovisualization research is increasingly dealing with the exploration of patterns and relationships in such large datasets for understanding underlying geographical processes. One of the attempts has been to use Artificial Neural Networks as a technology especially useful in situations where the numbers are vast and the relationships are often unclear or even hidden. RESULTS: We investigate ways to integrate computational analysis based on a Self-Organizing Map neural network, with visual representations of derived s...

One of directions of modem Intelligent Information Systems development is the creation of  computer systems for different medical applications. The role and meaning of those intelligent  systems will grow considerably with time, especially in connection with the problems of  telemedicine and medical informatics, The aim of this paper is to present possibilities of  application of methods of structural pattern analysis for the construction of intelligent visual and  information systems designed for application in medical information technology. Those systems  base their operation on syntactic reasoning with the use of graph grammars.  The paper shows that the proposed systems enable intelligent pattern understanding, owing to  which it is possible to recognize efficiently pathological lesions of dangerous diseases of  kidneys. Innovative features of the approach consists of a possibility to conduct, with the use of  proposed algorithms, a much deeper analysis directed at defining a semantic content of the  pattern analyzed. This in tum is an attempt to implement intelligent computer systems  understanding patterns.

Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image—Projection Explorer for Images—a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.