In our everyday life we interact with various information media, which present us with facts and opinions, supported with some evidence, based, usually, on condensed information extracted from data. It is common to communicate such condensed information .
Total Citations 3 Section: Part 1 --- Theory and MethodologiesClickstreams are among the most popular data sources because Web servers automatically record each action and the Web log entries promise to add up to a comprehensive description of behaviors of users. Clickstreams, however, are large and raise a number .
Total Citations 0Visual data mining, as an art and science of teasing meaningful insights out of large quantities of data that are incomprehensible in another way, requires consistent visual data representations (information visualisation models). The frequently used .
Total Citations 0Visualization in data mining is typically related to data exploration. In this chapter we present a methodology for the post processing and visualization of association rule models. One aim is to provide the user with a tool that enables the exploration .
Total Citations 0Frequent itemsets and association rules are defined on the powerset of a set of items and reflect the many-to-many relationships among the items. They bring technical challenges to information visualization which in general lacks effective visual .
Total Citations 1In today's applications data is produced at unprecedented rates. While the capacity to collect and store new data rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis .
Total Citations 65 Section: Part 2 --- TechniquesWe define, compute, and evaluate nested surfacesfor the purpose of visual data mining. Nested surfaces enclose the data at various density levels, and make it possible to equalize the more and less pronounced structures in the data. This .
Total Citations 1Association Rules are one of the most widespread data mining tools because they can be easily mined, even from very huge database, and they provide valuable information for many application fields such as marketing, credit scoring, business, etc. The .
Total Citations 1Visual data-mining strategy lies in tightly coupling the visualizations and analytical processes into one data-mining tool that takes advantage of the assets from multiple sources. This paper presents two graphical interactive decision tree construction .
Total Citations 0Support vector machines (SVM) offer a theoretically wellfounded approach to automated learning of pattern classifiers. They have been proven to give highly accurate results in complex classification problems, for example, gene expression analysis. The .
Total Citations 3The termvisual text analyticsdescribes a class of information analysis techniques and processes that enable knowledge discovery via the use of interactive graphical representations of textual data. These techniques enable discovery and .
Total Citations 3Visual discovery of network patterns of interaction between attributes in a data set identifies emergent networks between myriads of individual data items and utilises special algorithms that aid visualisation of `emergent' patterns and trends in the .
Total Citations 1This chapter introduces a novel systematization aiming at extending the application range of Information Visualization and Visual Data Mining. We present an innovative framework named Visualization Tree in order to integrate multiple data visualizations .
Total Citations 0This chapter presents VHHH: a visual data mining tool to compute and investigate hierarchical heavy hitters (HHHs) for two-dimensional data. VHHH computes the HHHs for a two-dimensional categorical dataset and a given threshold, and visualizes the HHHs .
Total Citations 0This chapter explores the extension of visual data mining by adding a sound dimension to the data representation. It presents the results of an early 2001 experiments with sonification of 2D and 3D time series data. A number of sonification means for .
Total Citations 0Context and history visualization plays an important role in visual data mining especially in the visual exploration of large and complex data sets. The preservation of context and history information in the visualization can improve user comprehension .
Total Citations 1As discussed in Part 1 of the book in chapter "Form-Semantics-Function --- A Framework for Designing Visualisation Models for Visual Data Mining" the development of consistent visualisation techniques requires systematic approach related to the tasks of .
Total Citations 0 Section: Part 3 --- Tools and ApplicationsA software system has been developed for the study of static and dynamic data visualization in the context of Visual Data Mining in Virtual Reality. We use a specific data set to illustrate how the visualization tools of the 3D Visual Data Mining (3DVDM).
Total Citations 2In this chapter we describe DataJewel, a new temporal data mining architecture. DataJewel tightly integrates a visualization component, an algorithmic component and a database component. We introduce a new visualization technique called CalendarView as .
Total Citations 0It cannot be overstated that the knowledge discovery process still presents formidable challenges. One of the main issues in knowledge discovery is the need for an overall framework that can support the entire discovery process. It is worth noting the .
Total Citations 0This chapter presents an integrative visual data mining approach towards biomedical data. This approach and supporting methodology are presented at a high level. They combine in a consistent manner a set of visualisation and data mining techniques that .
Total Citations 1Visual data-mining strategy lies in tightly coupling the visualizations and analytical processes into one data-mining tool that takes advantage of the strengths from multiple sources. We present concrete cooperation between automatic algorithms, .
Total Citations 1Ronnberg N Musical Elements in Sonification Support Visual Perception Proceedings of the 31st European Conference on Cognitive Ergonomics, (114-117)
Weld C and Leemis L Modeling mixed type random variables Proceedings of the 2017 Winter Simulation Conference, (1-12)
Ltifi H, Benmohamed E, Kolski C and Ben Ayed M (2016). Enhanced visual data mining process for dynamic decision-making, Knowledge-Based Systems , 112 :C , (166-181), Online publication date: 15-Nov-2016 .
Schulz A and Hammer B Visualization of Regression Models Using Discriminative Dimensionality Reduction Proceedings, Part II, of the 16th International Conference on Computer Analysis of Images and Patterns - Volume 9257, (437-449)
Stenholt R (2014). On the Benefits of Using Constant Visual Angle Glyphs in Interactive Exploration of 3D Scatterplots, ACM Transactions on Applied Perception , 11 :4 , (1-23), Online publication date: 9-Jan-2015 .
Bothorel G, Serrurier M and Hurter C From Visualization to Association Rules Proceedings of the 29th Spring Conference on Computer Graphics, (57-64)
Mozaffari E and Mudur S A classification scheme for characterizing visual mining Proceedings of the 1st international conference on Human interface and the management of information: interacting with information - Volume Part II, (46-54)
Nguyen Q, Simoff S and Huang M Interactive visualization with user perspective Proceedings of the 3rd International Symposium on Visual Information Communication, (1-6)
Koenig P, Zaidi F and Archambault D Interactive searching and visualization of patterns in attributed graphs Proceedings of Graphics Interface 2010, (113-120)