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In information science, profiling refers to the process of construction and application of profiles generated by computerized data analysis. This involves the use of algorithms or other mathematical techniques that allow the discovery of patterns or correlations in large quantities of data, aggregated in databases. When these patterns or correlations are used to identify or represent people, they can be called ''profiles''. Other than a discussion of profiling ''technologies'' or ''population profiling'', the notion of profiling in this sense is not just about the construction of profiles, but also concerns the ''application'' of group profiles to individuals, e. g., in the cases of credit scoring, price discrimination, or identification of security risks . Profiling is not simply a matter of computerized pattern-recognition; it enables refined price-discrimination, targeted servicing, detection of fraud, and extensive social sorting. Real-time machine profiling constitutes the precondition for emerging socio-technical infrastructures envisioned by advocates of ambient intelligence,〔 ISTAG (2001), Scenarios for Ambient Intelligence in 2010, Information Society Technology Advisory Group 〕 autonomic computing and ubiquitous computing . One of the most challenging problems of the information society involves dealing with increasing data-overload. With the digitizing of all sorts of content as well as the improvement and drop in cost of recording technologies, the amount of available information has become enormous and increases exponentially. It has thus become important for companies, governments, and individuals to discriminate information from noise, detecting useful or interesting data. The development of profiling technologies must be seen against this background. These technologies are thought to efficiently collect and analyse data in order to find or test knowledge in the form of statistical patterns between data. This process, called Knowledge Discovery in Databases (KDD) , provides the profiler with sets of correlated data usable as "profiles". == The profiling process == The technical process of profiling can be separated in several steps: * ''Preliminary grounding:'' The profiling process starts with a specification of the applicable problem domain and the identification of the goals of analysis. * ''Data collection:'' The target dataset or database for analysis is formed by selecting the relevant data in the light of existing domain knowledge and data understanding. * ''Data preparation:'' The data are preprocessed for removing noise and reducing complexity by eliminating attributes. * ''Data mining:'' The data are analysed with the algorithm or heuristics developed to suit the data, model and goals. * ''Interpretation:'' The mined patterns are evaluated on their relevance and validity by specialists and/or professionals in the application domain (e.g. excluding spurious correlations). * ''Application:'' The constructed profiles are applied, e.g. to categories of persons, to test and fine-tune the algorithms. * ''Institutional decision:'' The institution decides what actions or policies to apply to groups or individuals whose data match a relevant profile. Data collection, preparation and mining all belong to the phase in which the profile is under construction. However, profiling also refers to the application of profiles, meaning the usage of profiles for the identification or categorization of groups or individual persons. As can be seen in step six (application), the process is circular. There is a feedback loop between the construction and the application of profiles. The interpretation of profiles can lead to the reiterant – possibly real-time – fine-tuning of specific previous steps in the profiling process. The application of profiles to people whose data were not used to construct the profile is based on data matching, which provides new data that allows for further adjustments. The process of profiling is both dynamic and adaptive. A good illustration of the dynamic and adaptive nature of profiling is the Cross-Industry Standard Process for Data Mining (CRISP-DM). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Profiling (information science)」の詳細全文を読む スポンサード リンク
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