Data Mining Mehmed Kantardzic (good english books to read .txt) 📖

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 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 to augment the data-mining process. Some data-mining techniques and algorithms are difficult for decision makers to understand and use. Visualization can make the data and the mining results more accessible, allowing comparison and verification of results. Visualization can also be used to steer the data-mining algorithm.

It is useful to develop a taxonomy for data visualization, not only because it brings order to disjointed techniques, but also because it clarifies and interprets ideas and purposes behind these techniques. Taxonomy may trigger the imagination to combine existing techniques or discover a totally new technique.

Visualization techniques can be classified in a number of ways. They can be classified as to whether their focus is geometric or symbolic, whether the stimulus is 2-D, 3-D, or n-dimensional, or whether the display is static or dynamic. Many visualization tasks involve detection of differences in data rather than a measurement of absolute values. It is the well-known Weber’s Law that states that the likelihood of detection is proportional to the relative change, not the absolute change, of a graphical attribute. In general, visualizations can be used to explore data, to confirm a hypothesis, or to manipulate a view.

In exploratory visualizations, the user does not necessarily know what he/she is looking for. This creates a dynamic scenario in which interaction is critical. The user is searching for structures or trends and is attempting to arrive at some hypothesis. In confirmatory visualizations, the user has a hypothesis that needs only to be tested. This scenario is more stable and predictable. System parameters are often predetermined and visualization tools are necessary for the user to confirm or refute the hypothesis. In manipulative (production) visualizations, the user has a validated hypothesis and so knows exactly what is to be presented. Therefore, he/she focuses on refining the visualization to optimize the presentation. This type is the most stable and predictable of all visualizations.

The accepted taxonomy in this book is primarily based on different approaches in visualization caused by different types of source data. Visualization techniques are divided roughly into two classes, depending on whether physical data are involved. These two classes are scientific visualization and information visualization.

Scientific visualization