D.A. Keim. Visual Data Mining. Tutorials of the 23 rd International Conference on Very Large Data Bases, Athens, Greece, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... we propose an approach to assist the user in his data exploration based on the integration of different techniques such as data mining [Fayyad96] visualization [Wong97] and graphical interaction techniques [Keim02a] The idea is to assist the user with a visual data mining approach ( Keim02a] [Keim02b]) to discover new information in the database. The tool aids the user in data acquisition task offering both an automated presentation of data to dynamically visualize the metadata and interactive functionalities to discover the relationships among the different metadata attributes. 2. ....

Keim, D., and Mller, W., and Schumann, H. Visual Data Mining.in Eurographic STAR proc., Saarbrcken, 2002.

....and coplots, projection matrices, parallel coordinates, geometric projection techniques, icon based techniques, hierarchical techniques, dynamic techniques, graph based techniques, pixel oriented techniques, and combinations thereof. A complete comprehensive survey of these techniques appem s in [14, 15], and in [161 a detailed evaluation and comparison of several visual data mining techniques including pixel oriented, geometric and icon based techniques is made. It is importmir to note that while visualization techniques have the advantage of suggesting hitherto unknown hypotheses, they are ....

D. A. Keim. Visual data mining. Tutorial, Int. Conference on Very Large Databases (VLDB'97), 1997.

....and coplots, projection matrices, parallel coordinates, geometric projection techniques, icon based techniques, hierarchical techniques, dynamic techniques, graph based techniques, pixel oriented techniques, and combinations thereof. A complete comprehensive survey of these techniques appears in [17, 18], and in [19] a detailed evaluation and comparison of several visual data mining techniques including pixel oriented, geometric and icon based techniques is made. It is important to note that while visualization techniques have the advantage of suggesting hitherto unknown hypotheses, they are ....

D. A. Keim. Visual data mining. Tutorial, Int. Conference on Very Large Databases (VLDB'97), 1997.

....areas in the GRAIL browsing tool where improvement can be made are drawn out. 2.1 A Framework for Information Visualisation Design The design of visualisation is complex process. Many frameworks [14, 162, 163, 111, 85, 41, 42, 91, 73, 138, 176] cognitive models [99, 135, 123, 133] taxonomies [100, 98, 177, 84, 146, 181, 180], design principles [169, 86] and metrics [160, 96, 21] have already been proposed in relation to graphical information processing in general, and in 36 2.1. A FRAMEWORK FOR INFORMATION VISUALISATION DESIGN 37 some cases specifically developed for Information Visualisation. They are all aimed at ....

.... based on data type, visualisation styles and interactive styles [163] Shneiderman has developed a matrix taxonomy of data types (1D, 2D, 3D, Multidimensional, Network, Temporal) by various domain specific tasks types [146] Keim constructed a matrix of data types by visualisation graph types [84]. Card and Mackinlay [27] used tables to compare visualisation mappings by breaking down each data set into component types and enumerating the graphical mapping for each variable. 2.1. A FRAMEWORK FOR INFORMATION VISUALISATION DESIGN 50 2.1.6 Using the Framework To solve the GRAIL browsing ....

KEIM, D. Visual Data Mining. Tutorial in VLDB'97: International Conference on Very Large Databases, 1997. [Online] at: http://www.dbs.informatik. uni-muenchen.de/daniel/VLDBTutorial.ps.

....visualization techniques such as scatterplots [4] hyperslice [21] parallel coordinates [11] and circle segments [5] have been proposed. Variable resolution bivariate plots dynamically transition from scatterplot format to a greyscale format based on density estimates for each cell [10] See [15] for a survey of data visualization techniques. No previous technique speci cally addressed visualizing attribute dependence e ects while normalizing out the one dimensional distributions. 2 Zipf distributions are very frequently observed in practice for highly skewed data sets. In such ....

D.A. Keim. Visual Data Mining. Proceedings of VLDB, 1997.

....hyperslice (van Wijk van Liere 1993) parallel coordinates (Inselberg 1985) and circle segments (Ankerst, Keim, Kriegel 1996) have been proposed. No previous technique specifically addressed visualizing attribute dependence effects while normalizing out the one dimensional distributions. See (Keim 1997) for a survey. There is much literature on finding histogram bucket boundaries (Ioannidis Poosala 1995; Poosala et al. 1996; Jagadish et al. 1998) There is even some literature on creating histogram bins in multiple dimensions (Poosala Ioannidis 1997; Berchtold, Jagadish, Ross 1998) ....

Keim, D. 1997. Visual Data Mining. Proceedings of VLDB.

....hyperslice (van Wijk van Liere 1993) parallel coordinates (Inselberg 1985) and circle segments (Ankerst, Keim, Kriegel 1996) have been proposed. No previous technique specifically addressed visualizing attribute dependence effects while normalizing out the one dimensional distributions. See (Keim 1997) for a survey. There is much literature on finding histogram bucket boundaries (Ioannidis Poosala 1995; Poosala et al. 1996; Jagadish et al. 1998) There is even some literature on creating histogram bins in multiple dimensions (Poosala Ioannidis 1997; Berchtold, Jagadish, Ross 1998) ....

Keim, D. 1997. Visual Data Mining. Proceedings of VLDB.

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D.A. Keim. Visual Data Mining. Tutorials of the 23 rd International Conference on Very Large Data Bases, Athens, Greece, 1997.

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Daniel A. Keim. Visual data mining (tutorial). In Int. Conf. on Very Large Databases (VLDB'97),Athens, Greece, 1997.

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D. Keim, W. M uller, and H. Schumann. Visual data mining. In EUROGRAPHICS 2002.

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