From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> An algorithm for the analysis of multivariate data is presented along with some experimental results. The algorithm is based upon a point mapping of N L-dimensional vectors from the L-space to a lower-dimensional space such that the inherent data "structure" is approximately preserved. <s> BIB001 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> Describes a query system that provides visual relevance feedback in querying large databases. The goal is to support the process of data mining by representing as many data items as possible on the display. By arranging and coloring the data items as pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. Furthermore, by using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. The system allows one to represent the largest amount of data that can be visualized on current display technology, provides valuable feedback in querying the database, and allows the user to find results which would otherwise remain hidden in the database. > <s> BIB002 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> In this paper , we describe a novel technique for visualizing large amounts of high-dimensio nal data, called ‘circle segments’. The technique uses one colored pixel per data value and can therefore be classified as a pixel-per-value technique [Kei 96]. The basic idea of the ‘circle segments’ visualization technique is to display the data dimensions as segments of a circle. If the data consists of k dimensions, the circle is partitioned into k segments, each representing one data dimension. Inside the segments, the data values belonging to one dimension are arranged from the center of the circle to the outside in a back and forth manner orthogonal to the line that halves the segment. Our first results show that the ‘circle segment’ technique is very powerful for visualizing large amounts of data, providing more expressive visualizations than other wellknown techniques such as the ‘recursive pattern’ technique and traditional ‘line graphs’. <s> BIB003 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> The display of multivariate datasets in parallel coordinates, transforms the search for relations among the variables into a 2-D pattern recognition problem. This is the basis for the application to visual data mining. The knowledge discovery process together with some general guidelines are illustrated on a dataset from the production of a VLSI chip. The special strength of parallel coordinates is in modeling relations. As an example, a simplified economic model is constructed with data from various economic sectors of a real country. The visual model shows the interelationship and dependencies between the sectors, circumstances where there is competition for the same resource, and feasible economic policies. Interactively, the model can be used to do trade-off analyses, discover sensitivities, do approximate optimization, monitor (as in a process) and provide decision support. <s> BIB004 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> Describes data exploration techniques designed to classify DNA sequences. Several visualization and data mining techniques were used to validate and attempt to discover new methods for distinguishing coding DNA sequences (exons) from non-coding DNA sequences (introns). The goal of the data mining was to see whether some other, possibly non-linear combination of the fundamental position-dependent DNA nucleotide frequency values could be a better predictor than the AMI (average mutual information). We tried many different classification techniques including rule-based classifiers and neural networks. We also used visualization of both the original data and the results of the data mining to help verify patterns and to understand the distinction between the different types of data and classifications. In particular, the visualization helped us develop refinements to neural network classifiers, which have accuracies as high as any known method. Finally, we discuss the interactions between visualization and data mining and suggest an integrated approach. <s> BIB005 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> The major challenge facing the bioinformatics community is the continuing increase in the number, size and complexity of biological databases with which it must contend. The goal of the research discussed herein is the development and utilisation of techniques that allow researchers to extract new and useful information from these burgeoning information resources using advanced visualisation methods and paradigms, coupled with distributed object technologies that allow communications between applications and remote databases. Visualisation has roles not only in analysis, but also in building more user-friendly interfaces, implementing methods to navigate large information spaces intuitively and powerful techniques to browse and query data. By using platform-independent object-oriented programming languages, these resources may be developed as reusable pieces of software componentry with their methods and interfaces defined fully, and then distributed through organisations such as the bioWidget Consortium. The widget and object-oriemed approach is a powerful paradigm in developing new applications from existing components. Development time is reduced and greater time is spent on analysing these data, rather than in the writing of monolithic applications. More powerful applications can be constructed from components interacting in concert and offers the opportunity of a new generation of bioinformatics tools. <s> BIB006 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> Visualization tools for bioinformatics ideally should provide universal access to the most current data in an interactive and intuitive graphical user interface. Since the introduction of Java, a language designed for distributed programming over the Web, the technology now exists to build a genomic data visualization tool that meets these requirements. Using Java we have developed a prototype genome browser applet (BioViews) that incorporates a three-level graphical view of genomic data: a physical map, an annotated sequence map, and a DNA sequence display. Annotated biological features are displayed on the physical and sequence-based maps, and the different views are interconnected. The applet is linked to several databases and can retrieve features and display hyperlinked textual data on selected features. In addition to browsing genomic data, different types of analyses can be performed interactively and the results of these analyses visualized alongside prior annotations. Our genome browser is built on top of extensible, reusable graphic components specifically designed for bioinformatics. Other groups can (and do) reuse this work in various ways. Genome centers can reuse large parts of the genome browser with minor modifications, bioinformatics groups working on sequence analysis can reuse components to build front ends for analysis programs, and biology laboratories can reuse components to publish results as dynamic Web documents. <s> BIB007 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> An important issue in data mining is the recognition of complex dependencies between attributes. Past techniques for identifying attribute dependence include correlation coefficients, scatterplots, and equiwidth histograms. These techniques are sensitive to outliers, and often are not sufficiently informative to identify the kind of attribute dependence present. ::: ::: We propose a new approach, which we call independence diagrams. We divide each attribute into ranges; for each pair of attributes, the combination of these ranges defines a two-dimensional grid. For each cell of this grid, we store the number of data items in it. We display the grid, scaling each attribute axis so that the displayed width of a range is proportional to the total number of data items within that range. The brightness of a cell is proportional to the density of data items in it. ::: ::: As a result, both attributes are independently normalized by frequency, ensuring insensitivity to outliers and skew, and allowing specific focus on attribute dependencies. Furthermore, independence diagrams provide quantitative measures of the interaction between two attributes, and allow formal reasoning about issues such as statistical significance. <s> BIB008 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. Finally, a brief overview of the WebSIFT system as an example of a prototypical Web usage mining system is given. <s> BIB009 </s> From visual data exploration to visual data mining: a survey <s> Visual Data Exploration for Mining <s> For many companies, competitiveness in e-commerce requires a successful presence on the web. Web sites are used to establish the company's image, to promote and sell goods and to provide customer support. The success of a web site affects and reflects directly the success of the company in the electronic market. In this study, we propose a methodology to improve the ``success'' of web sites, based on the exploitation of navigation pattern discovery. In particular, we present a theory, in which success is modelled on the basis of the navigation behaviour of the site's users. We then exploit WUM, a navigation pattern discovery miner, to study how the success of a site is reflected in the users' behaviour. With WUM we measure the success of a site's components and obtain concrete indications of how the site should be improved. We report on our first experiments with an online catalog, the success of which we have studied. Our mining analysis has shown very promising results, on the basis of which the site is currently undergoing concrete improvements. <s> BIB010
Mining tasks usually demand techniques capable of handling large amounts of multidimensional data, often in the format of Data Tables or relational databases. Parallel coordinates and scatter plots are much exploited in this context, as shown by the examples drawn from different application areas. Also, interaction mechanisms for filtering, querying, and selecting data are typically required for handling larger data sets. This point is strongly emphasized by Inselberg BIB004 in a paper illustrating the strength of parallel coordinates integrated with effective interactive query mechanisms for providing visual cues in a discovery process. The papers discussed in this section illustrate applications of visual data exploration tools to real problems in different domains. Symanzik et al.  describe how the statistical graphics package XGobi has been used for visual mining of data describing the response of neuron cells to electrical stimuli. They simulated physiological response from three-dimensional neuroanatomical data from which morphological measurements are obtained, with the goal of exploring the neuromorphological effects on the electrical response of cells. Using the brushing-tour strategy and linked brushing in scatter plots and dot plots, they identified apparent correlation of electrophysiological behavior and certain morphometric parameters that characterize cell morphology. Hoffman et al. BIB005 provide a case study describing how high-dimensional visual data exploration techniques such as RadViz, Parallel Coordinates, and Sammon Plots BIB001 have been used in combination with rule-based classifiers and neural networks to classify DNA sequences. Cvek et al.  used analytic and visualization techniques for mining yeast functional genomics data sets. They compare several classification and clustering techniques on both data sets, showing how the application of Parallel Coordinates, Circle Segments BIB003 , and RadViz helped gain insight into the data, as well as to visually compare and contrast the analytical techniques. These and other papers and Web sites  describing mining and visualization tools applied to Bioinformatics BIB007 , BIB006 clearly show this domain as one that poses many challenges to researchers working in mining and visualization. In an application to e-commerce, Lee and Podlaseck [61] analyze clickstreams, i.e., the series of links followed by customers of an electronic commerce site. They describe an interactive e-commerce visualization system for web merchandising analysis that supports users in interpreting and exploring clickstream data of online stores. Their system includes facilities for zooming, filtering, color coding, dynamic querying, and data sampling, in addition to parallel coordinates and scatter plot visualizations. They describe an empirical case study with clickstream data from an online retailer in which the visualizations operate on a set of metrics defined from online merchandising analysis. Parallel coordinates and scatter plots are used, respectively, to analyze user sessions and session data integrated with basket placements and transactions extracted from the e-commerce server. Some authors actually propose new visual techniques targeted at supporting particular DM tasks. The work by Keim et al. BIB002 was one of the first to describe a database query interface based on a visualization technique designed to provide users with visual feedback on their queries. Cadez et al.  describe an approach for exploratory analysis and visualization of the dynamic behavior of visitors of a particular Web site. The application of mining to data collected from web server logs is an important research trend  , BIB009 ,  , BIB010 , due to the importance of understanding user behavior in the context of digital environments in general and the web in particular. Cadez et al.  focus on clustering users with similar behavior and then visualizing the behavior of users within a cluster. Their visualization tool uses multiple windows to display user data regarding the multiple clusters. Sequences of rows within a window (see Fig. 8 ) show the paths of single users through the site, each path being color coded by category (categories reflect the different types of service provided by the one particular site analyzed). The tool can help site administrators in identifying navigation patterns that may actually suggest actions to be taken to improve the site. In addition to the detailed view of each cluster, the tool also provides summary information about clusters. Independence Diagrams BIB008 is a technique aimed at the recognition of complex dependencies between data attributes, a common DM task. It displays dependencies between two data attributes, providing an alternative to scatter plot diagrams that is insensitive to data skew and outliers. The technique works by dividing the two attributes of interest independently into slices (i.e., rows or columns) such that each slice has roughly the same number of data items and, additionally, splitting slices having a large extension. Each intersection of row and column defines a 2D bucket for which a count of the data items contained is stored. This grid of slices/buckets is mapped to the screen so that the width of a cell on the screen is proportional to the number of items in its corresponding bucket and the brightness of the cell is proportional to the number of data items in its corresponding slice, as illustrated in Fig. 9 . The authors state that, after some training, even nonexpert users can make quantitative judgements based on the data displays. The limitation is that only pairs of attributes can be analyzed with this technique. Another basic task in data analysis and pattern recognition is classification and Inselberg and Avidan  describe a geometrically motivated classification algorithm that exploits properties of the representation of multidimensional objects in the Parallel Coordinates visualization technique. Their classifier has low polynomial worst-case complexity in the number of variables and data set size, thus allowing dynamic derivation of rules in near real time. They test their classifier on three classification benchmark data sets, with very good results as far as test error rates are concerned. Dy and Brodley  tackle the feature selection problem, a step that usually precedes clustering to identify a feature subset that best discovers data clusters. They introduce a visual feature subset selection approach that incorporates visualization techniques, clustering, and user interaction to guide the feature subset search by a human. This is an alternative to automated feature selection, which may be a difficult task when coupled with unsupervised learning. Their approach relies on scatter plots for visualization: They use Linear Discriminant Analysis to project the data to 2D and display the data and the different cluster structures as 2D scatter plots. The approach is illustrated on a lung image data set.