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VISUAL DM

data mining: opportunities and challenges

Chapter XX - Critical and Future Trends in Data Mining—A Review of Key Data Mining Technologies/Applications
Data Mining: Opportunities and Challenges
by John Wang (ed)
Idea Group Publishing 2003


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VISUAL DM

Visual DM is an emerging area of explorative and intelligent data analysis and mining, based on the integration of concepts from computer graphics, visualization metaphors and methods, information and scientific data visualization, visual perception, cognitive psychology, diagrammatic reasoning, visual data formatting, and 3-D collaborative virtual environments. Research and developments in the methods and techniques for visual DM have helped to identify many of the research directions in the field, including visual methods for data analysis, visual DM process models, visual reasoning and uncertainty management, visual explanations , algorithmic animation methods, perceptual and cognitive aspects, and interactivity in visual DM. Other key areas include the study of domain knowledge in visual reasoning, virtual environments, visual analysis of large DBs, collaborative exploration and model building, metrics for evaluation, generic system architectures and prototypes , and methods for visualizing semantic content (Han & Kamber, 2001).


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Chapter XX - Critical and Future Trends in Data Mining—A Review of Key Data Mining Technologies/Applications
Data Mining: Opportunities and Challenges
by John Wang (ed)
Idea Group Publishing 2003


	Brought to you by Team-Fly

MULTIMEDIA DM

Multimedia DM is the mining and analysis of various types of data, including images, video, audio, and animation. The idea of mining data that contains different kinds of information is the main objective of multimedia DM (Zaiane, Han, Li, & Hou, 1998). As multimedia DM incorporates the areas of text mining as well as hypertext/hypermedia mining, these fields are closely related . Much of the information describing these other areas also applies to multimedia DM. This field is also rather new, but holds much promise for the future. A developing area in multimedia DM is that of audio DM (mining music). The idea is basically to use audio signals to indicate the patterns of data or to represent the features of DM results. It is possible not only to summarize melodies based on the approximate patterns that repeatedly occur in the segment, but also to summarize style based on tone, tempo, or the major musical instruments played (Han & Kamber, 2001; Zaiane, Han, Li, & Hou, 1998; Zaiane, Han, & Zhu, 2000).


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Chapter XX - Critical and Future Trends in Data Mining—A Review of Key Data Mining Technologies/Applications
Data Mining: Opportunities and Challenges
by John Wang (ed)
Idea Group Publishing 2003


	Brought to you by Team-Fly

SPATIAL AND GEOGRAPHIC DM

Aside from statistical or numeric data, it is also important to consider information that is of an entirely different kind-i.e., spatial and geographic data that could contain information about astronomical data, natural resources, or even orbiting satellites and spacecraft that transmit images of earth from out in space. Much of this data is image-oriented and can represent a great deal of information if properly analyzed and mined (Miller & Han, 2001). Analyzing spatial and geographic data includes such tasks as understanding and browsing spatial data, uncovering relationships between spatial data items (and also between non-spatial and spatial items), and also analysis using spatial DBs and spatial knowledge bases. The applications of these would be useful in such fields as remote sensing, medical imaging, navigation, and related uses. Some of the techniques and data structures that are used to analyze spatial and related types of data include the use of spatial warehouses, spatial data cubes, and spatial On Line Analytic Processing (OLAP). Spatial data warehouses can be defined as those that are subject-oriented, integrated, nonvolatile, and time-variant (Han, Kamber, & Tung, 2001). Aside from the implementation of data warehouses for spatial data, there is also the issue of analyses that can be done on the data. Some of the analyses that can be done include association analysis, clustering methods , and the mining of raster DBs. There have been a number of studies conducted on spatial DM (Bedard, Merritt, & Han 2001; Han, Kamber & Tung, 1998; Han, Koperski, & Stefanovic, 1997; Han, Stefanovic, & Koperski, 1998; Koperski, Adikary, & Han, 1996; Koperski & Han, 1995; Koperski, Han, & Marchisio, 1999; Koperski, Han, & Stefanovic, 1998; Tung, Hou, & Han, 2001).


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