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Data Mining and Profiling in Big Data
Data mining and profiling are technologies used for analyzing and interpreting large amounts of data (a set of facts) to obtain knowledge (patterns in the data that are interesting and certain enough for a user). In the information society, vast amounts of data are collected, stored, and processed by both public and private organizations. When dealing with large data sets, particularly in the context of big data, human intuition may be insufficient to obtain insight into or an overview of the data available.
Data mining and group profiling are considered separate technologies, even though they are often used together. Whereas the focus of data mining is on finding novel patterns and relations in data sets, the focus of profiling is on ascribing characteristics to individuals or groups of people. Profiling may be carried out without the use of data mining, and vice versa. In some cases, profiling may not involve (much) technology—for instance, when psychologically profiling a serial killer.
Advantages and Disadvantages
Profiles may offer general advantages, such as enabling the selection of target groups, customization, and cost efficiency. For corporations, profiles may be useful to identify new customers, personalize special offers, evaluate the profitability of product groups, and assess credit scores. Particularly, banks and insurance companies are interested in risk profiles to determine to whom to provide loans, mortgages, and insurances and under which conditions. For government agencies, profiles may be useful to identify target groups for their policies, to evaluate their policies, and to optimize public services. Particularly, criminal investigation organizations, including police agencies, and intelligence organizations are interested in risk profiles to identify criminals and terrorists, to assess and predict where crime will take place (so-called hotspots), and to disclose criminal networks.
General disadvantages of group profiles may involve, for instance, unjustified discrimination (e.g., when profiles contain sensitive characteristics like ethnicity or gender, which are used for decision making), stigmatization (when profiles become public knowledge), dehumanization (regarding people as data sets rather than human beings), de-individualization (regarding people as parts of groups rather than unique individuals), loss of privacy (when predicting characteristics that people do not want to disclose), loss of autonomy (as data mining and profiling practices may not be very transparent), and being confronted with unwanted information (e.g., with life expectancies). Many of the effects of group profiles may be considered advantageous as well as disadvantageous, depending on the context and the way in which, and by whom, the group profile is used.
Data Mining
Data mining is an automated analysis of data, using mathematical algorithms, to find new patterns and relations in (large amounts of) data. Data mining is a step in a process called knowledge discovery in databases. Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. This process consists of five successive steps: (1) data collection, (2) data preparation, (3) data mining, (4) interpretation, and (5) determining actions. Hence, the third step is the actual data mining stage, in which the data are analyzed to find certain patterns or relations. This is done using mathematical algorithms. Data mining is different from traditional database techniques or statistical methods because what is being looked for does not necessarily have to be known. Thus, data mining may be used to discover new patterns or to confirm suspected relationships. The former is called a bottom-up or data-driven approach, because it starts with the data and then theories based on the discovered patterns are built. The latter is called a top-down or theory-driven approach, because it starts with a hypothesis and then the data are checked to determine whether they are consistent with the hypothesis.
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