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in terms of data; they are inspired by and think in terms of images—mental pictures of a given situation—and they assimilate information more quickly and effectively as visual images than as textual or tabular forms. Human vision is still the most powerful means of sifting out irrelevant information and detecting significant patterns. The effectiveness of this process is based on a picture’s submodalities (shape, color, luminance, motion, vectors, texture). They depict abstract information as a visual grammar that integrates different aspects of represented information. Visually presenting abstract information, using graphical metaphors in an immersive 2-D or 3-D environment, increases one’s ability to assimilate many dimensions of the data in a broad and immediately comprehensible form. It converts aspects of information into experiences our senses and mind can comprehend, analyze, and act upon.

We have heard the phrase “Seeing is believing” many times, although merely seeing is not enough. When you understand what you see, seeing becomes believing. Recently, scientists discovered that seeing and understanding together enable humans to discover new knowledge with deeper insight from large amounts of data. The approach integrates the human mind’s exploratory abilities with the enormous processing power of computers to form a powerful visualization environment that capitalizes on the best of both worlds. A computer-based visualization technique has to incorporate the computer less as a tool and more as a communication medium. The power of visualization to exploit human perception offers both a challenge and an opportunity. The challenge is to avoid visualizing incorrect patterns leading to incorrect decisions and actions. The opportunity is to use knowledge about human perception when designing visualizations. Visualization creates a feedback loop between perceptual stimuli and the user’s cognition.

Visual data-mining technology builds on visual and analytical processes developed in various disciplines including scientific visualization, computer graphics, data mining, statistics, and machine learning with custom extensions that handle very large multidimensional data sets interactively. The methodologies are based on both functionality that characterizes structures and displays data and human capabilities that perceive patterns, exceptions, trends, and relationships.

15.2 SCIENTIFIC VISUALIZATION AND INFORMATION VISUALIZATION

Visualization is defined in the dictionary as “a mental image.” In the field of computer graphics, the term has a much more specific meaning. Technically, visualization concerns itself with the display of behavior and, particularly, with making complex states of behavior comprehensible to the human eye. Computer visualization, in particular, is about using computer graphics and other techniques to think about more cases, more variables, and more relations. The goal is to think clearly, appropriately, with insight, and to act with conviction. Unlike presentations, visualizations are typically interactive and very often animated.

Because of the high rate of technological progress, the amount of data stored in databases increases rapidly. This proves true for traditional relational databases and complex 2-D and 3-D multimedia databases that store images, computer-aided design (CAD) drawings, geographic information, and molecular biology structure. Many of the applications mentioned rely on very large databases consisting of millions of data objects with several tens to a few hundred dimensions. When confronted with the complexity of data, users face tough problems: Where do I start? What looks interesting here? Have I missed anything? What are the other ways to derive the answer? Are there other data available? People think iteratively and ask ad hoc questions of complex data while looking for insights.

Computation, based on these large data sets and databases, creates content. Visualization makes computation and its content accessible to humans. Therefore, visual data mining uses visualization