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Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. [1] Data classification is typically a manual process; however, there are tools that can help gather information about the data. [2] Data sensitivity levels are often proposed to be considered. [2]
A formal security clearance is required to view or handle classified material. The clearance process requires a satisfactory background investigation. Documents and other information must be properly marked "by the author" with one of several (hierarchical) levels of sensitivity—e.g. restricted, confidential, secret, and top secret.
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Material that is classified as Unclassified // For Official Use Only (U//FOUO) is considered between Unclassified and Confidential and may deal with employee data. [ citation needed ] For access to information at a given classification level, individuals must have been granted access by the sponsoring government organization at that or a higher ...
These systems enforce the classification and labeling rules described above in software. Since 2005 they are not considered secure enough to allow uncleared users to share computers with classified activities. Thus, if one creates an unclassified document on a secret device, the resultant data is classified secret until it can be manually reviewed.
An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." [12] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a ...
Data mining – Process of extracting and discovering patterns in large data sets; Data warehouse – Centralized storage of knowledge; Fuzzy logic – System for reasoning about vagueness; Information retrieval – Obtaining information resources relevant to an information need; List of datasets for machine learning research
Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. In almost all systems and processes, the application of affinity analysis can extract significant knowledge about the unexpected trends [ citation needed ] .