Data Mining - Mehmed Kantardzic [249]
Pal, S. K., S. Mitra, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, Inc., New York, 1999.
The authors consolidate a wealth of information previously scattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern-recognition tasks using neuro-fuzzy networks—classification, feature evaluation, rule generation, and knowledge extraction. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms to ensure a more efficient recognition system.
Pedrycz, W., F. Gomide, An Introduction to Fuzzy Sets: Analysis and Design, The MIT Press, Cambridge, 1998.
The book provides a highly readable, comprehensive, self-contained, updated, and well-organized presentation of the fuzzy-set technology. Both theoretical and practical aspects of the subject are given a coherent and balanced treatment. The reader is introduced to the main computational models, such as fuzzy modeling and rule-based computation, and to the frontiers of the field at the confluence of fuzzy-set technology with other major methodologies of soft computing.
15
VISUALIZATION METHODS
Chapter Objectives
Recognize the importance of a visual-perception analysis in humans to discover appropriate data-visualization techniques.
Distinguish between scientific-visualization and information-visualization techniques (IVT).
Understand the basic characteristics of geometric, icon-based, pixel-oriented, and hierarchical techniques in visualization of large data sets
Explain the methods of parallel coordinates and radial visualization for n-dimensional data sets.
Analyze the requirements for advanced visualization systems in data mining.
How are humans capable of recognizing hundreds of faces? What is our “channel capacity” when dealing with the visual or any other of our senses? How many distinct visual icons and orientations can humans accurately perceive? It is important to factor all these cognitive limitations when designing a visualization technique that avoids delivering ambiguous or misleading information. Categorization lays the foundation for a well-known cognitive technique: the “chunking” phenomena. How many chunks can you hang onto? That varies among people, but the typical range forms “the magical number seven, plus or minus two.” The process of reorganizing large amounts of data into fewer chunks with more bits of information per chunk is known in cognitive science as “recoding.” We expand our comprehension abilities by reformatting problems into multiple dimensions or sequences of chunks, or by redefining the problem in a way that invokes relative judgment, followed by a second focus of attention.
15.1 PERCEPTION AND VISUALIZATION
Perception is our chief means of knowing and understanding the world; images are the mental pictures produced by this understanding. In perception as well as art, a meaningful whole is created by the relationship of the parts to each other. Our ability to see patterns in things and pull together parts into a meaningful whole is the key to perception and thought. As we view our environment, we are actually performing the enormously complex task of deriving meaning out of essentially separate and disparate sensory elements. The eye, unlike the camera, is not a mechanism for capturing images so much as it is a complex processing unit that detects changes, forms, and features, and selectively prepares data for the brain to interpret. The image we perceive is a mental one, the result of gleaning what remains constant while the eye scans. As we survey our three-dimensional (3-D) ambient environment, properties such as contour, texture, and regularity allow us to discriminate objects and see them as constants.
Human beings do not normally think