Visual data mining

A very important data mining task is the discovery of characteristic descriptions for subsets of data, which characterize its members and distinguish it from other subsets. Descriptions can, for example, be the output of statistical methods like average or variance. 

Data mining in chemistry focuses on the extraction and evaluation of information in chemical data sets. In contrast to other fields of data mining applications, chemical data mining does not confine itself to conventional database queries but rather generates new information from the data. 

1000 words . Therefore, computer graphical representations allow a very effective and easy analysis of usually high-dimensional, complex, and extremely large chemical data sets. Furthermore, graphical representations facilitate the communication between scientists and decision-makers. 

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To understand visual data mining and information visualization techniques... 

Handling of complex data sets Visual data mining methods especially show huge advantages over classical approaches if only Httle information about the data is known or if the expected patterns and relationships are not clearly defined. Furthermore, very inhomogeneous data sets or data with a high noise level can still be analyzed by these methods. 

The explorative analysis of data sets by visual data mining applications takes place in a three-step process During the first step (overview), the user can obtain an overview of the data and maybe can identify some basic relationships between specific data points. In the second step (filtering), dynamic and interactive navigation, selection, and query tools will be used to reorganize and filter the data set. Each interaction by the user will lead to an immediate update of the data scene and will reveal the hidden patterns and relationships. Finally, the patterns or data points can be analyzed in detail with specific detail tools. 

Visual data mining allows the visualization and detection of hidden relationships in sets of data. 

D. Keim, Information Visualization and Visual Data Mining, IEEE Trans. Visualization and Computer Graphics, 2002, 8,100-107. 

STATISTICA StatSoft Provides a comprehensive and integrated set of tools and solutions for data visualization, graphical data analysis, visual data mining, visual querying (http //www.statsoft.com/unique-features/statistica-general-overview/)... 

Integrative visual data-mining of multi-scale Bio-Network/Pathways (GO compliant) Protein-protein interaction graphs visualisation tool Platform for visualizing and integrating molecular interactions including mRNA expression profile (GO compliant)... 

Eerteua de OUveira, M. C., and Levkowitz, H. (2003). Etom visual data exploration to visual data mining A survey. IEEE Trans. Visualizat. Comput. Graphics, 9(3) 378-394. 

It extends the usage of statistical methods and combines it with machine learning methods and the application of expert systems. The visualization of the results of data mining is an important task as it facilitates an interpretation of the results. Figure 9-32 plots the different disciplines which contribute to data mining. 

Data Mining is the core of the more comprehensive process of knowledge dis-coveiy in data bases (KDD). However, the term data mining" is often used synonymously with KDD. KDD describes the process of extracting and storing data and also includes methods for data preparation such as data cleaning, data selection, and data transformation as well as evaluation, presentation, and visualization of the results after the data mining process. 

Higher quality of the resulting patterns The natural capabiUty of human beings to visually recognize patterns and relations can be used and leads to a more effective data mining process. 

U. Fayyad, G. Grinstein, A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufman Publishers, San Frandsco, USA, 2002. 

NLP systems are being developed to address the increasingly challenging problem of data mining for systems level content from the published literature, that is, integrating across the global expert database of biomedical research [71]. One recent approach to this problem was to develop a web-based tool, PubNet, that is able to visualize concept and theme networks derived from the PubMed literature [72]. 

The described computational tools provide interactive, fast, and flexible data visualizations of chemical data that help and even enhance the human thought processes. However, visualization alone is often inadequate when multiple data points must be considered. A number of data mining methods that seek to identify significant relationships in large multidimensional databases are now being used for library design. 

Konig A. Interactive visualization and analysis of hierarchical neural projections for data mining. IEEE Trans Neural Networks 2000 11 615-24. 

MetaBase is a curated database of human protein-protein and protein-DNA interactions, transcriptional factors, signaling, metabolism and bioactive molecules. MetaCore provides intuitive tools for data visualization, mapping and exchange, multiple networking algorithms and data mining. 

Bioinformatics Robotics control, image processing, data mining, and visualization are usually used for implementation of microarray experiments. 

Applications of molecular descriptors are as diverse as their definitions. The important classes of applications include QSAR and/or QSPR, similarity, diversity, predictive models for virtual screening and/or data mining, data visualization. We will discuss briefly some of these applications in the next sections. 

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