Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

the filtered data, which efficiently conveys the relevant and meaningful information. Finally, the representation is often rendered to communicate information to the human user.

Data visualization is essential for understanding the concept of multidimensional spaces. It allows the user to explore the data in different ways and at different levels of abstraction to find the right level of details. Therefore, techniques are most useful if they are highly interactive, permit direct manipulation, and include a rapid response time. The analyst must be able to navigate the data, change its grain (resolution), and alter its representation (symbols, colors, etc.).

Broadly speaking, the problems addressed by current information-visualization tools and requirements for a new generation fall into the following classes:

1. Presentation Graphics. These generally consist of bars, pies, and line charts that are easily populated with static data and drop into printed reports or presentations. The next generation of presentation graphics enriches the static displays with a 3-D or projected n-dimensional information landscape. The user can then navigate through the landscape and animate it to display time-oriented information.

2. Visual Interfaces for Information Access. They are focused on enabling users to navigate through complex information spaces to locate and retrieve information. Supported user tasks involve searching, backtracking, and history logging. User-interface techniques attempt to preserve user-context and support smooth transitions between locations.

3. Full Visual Discovery and Analysis. These systems combine the insights communicated by presentation graphics with an ability to probe, drill down, filter, and manipulate the display to answer the “why” question as well as the “what” question. The difference between answering a “what” and a “why” question involves an interactive operation. Therefore, in addition to the visualization technique, effective data exploration requires using some interaction and distortion techniques. The interaction techniques let the user directly interact with the visualization. Examples of interaction techniques include interactive mapping, projection, filtering, zooming, and interactive linking and brushing. These techniques allow dynamic changes in the visualizations according to the exploration objectives, but they also make it possible to relate and combine multiple, independent visualizations. Note that connecting multiple visualizations by linking and brushing, for example, provides more information than considering the component visualizations independently. The distortion techniques help in the interactive exploration process by providing a means for focusing while preserving an overview of the data. Distortion techniques show portions of the data with a high level of detail while other parts are shown with a much lower level of detail.

Three tasks are fundamental to data exploration with these new visualization tools:

1. Finding Gestalt. Local and global linearities and nonlinearities, discontinuities, clusters, outliers, unusual groups, and so on are examples of gestalt features that can be of interest. Focusing through individual views is the basic requirement to obtain a qualitative exploration of data using visualization. Focusing determines what gestalt of the data is seen. The meaning of focusing depends very much on the type of visualization technique chosen.

2. Posing Queries. This is a natural task after the initial gestalt features have been found, and the user requires query identification and characterization technique. Queries can concern individual cases as well as subsets of cases. The goal is essentially to find intelligible parts of the data. In graphical data analysis it is natural to pose queries graphically. For example, familiar brushing techniques such as coloring or otherwise highlighting a subset of data means issuing a query about this subset. It is desirable that the view where the query is posed and the view that present the response are linked. Ideally, responses to queries should be instantaneous.

3. Making Comparisons. Two types of comparisons are frequently made in practice. The first one is a comparison of variables or projections and the second one is a comparison of subsets of data. In the first case, one compares views “from different angles”; in the second, comparison is based on views “of different slices” of the data. In either case, it is likely that a large number of plots are generated, and therefore it is a challenge to organize the plots in such a way that meaningful comparisons are possible.

Visualization has been used routinely in data mining as a presentation tool to generate initial views, navigate data with complicated structures, and convey the results of an analysis. Generally, the analytical methods themselves do not involve visualization. The loosely coupled relationships between visualization and analytical data-mining techniques represent the majority of today’s state-of-the-art in visual data mining. The process-sandwich strategy, which interlaces analytical processes with graphical visualization, penalizes both procedures with the other’s deficiencies and limitations. For example, because an analytical process cannot analyze multimedia data, we have to give up the strength of visualization to study movies and music in a visual data-mining environment. A stronger strategy lies in tightly coupling the visualization and analytical processes into one data-mining tool. Letting human visualization participate in the decision making in analytical processes remains a major challenge. Certain mathematical steps within an analytical procedure may be substituted by human decisions based on visualization to allow the same procedure to analyze a broader scope of information. Visualization supports humans in dealing with decisions that can no longer be automated.

For example, visualization techniques can be used for efficient process of “visual clustering.” The algorithm is based on finding a set of projections P = [P1, P2, … ,Pk] useful for separating the initial data into clusters. Each projection represents the histogram information of the point density in the projected space. The most important information about a projection is whether it contains well-separated clusters. Note that well-separated clusters in one projection could result from more than one cluster in the original space. Figure 15.10 shows an illustration of these projections. You can see that the axes’ parallel projections do not preserve well the information necessary for clustering. Additional projections A and B, in Figure 15.10, define three clusters in the initial data set.

Figure 15.10. An example of the need for general projections, which are not parallel to axes, to improve clustering process.