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Data Mining in Public Administration
Abstract
Data mining involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. In order to complete these tasks, the contemporary data mining packages offer techniques such as neural networks, inductive learning decision trees, cluster analysis, link analysis, genetic algorithms, visualization, and so forth (Hand, Mannila, & Smyth, 2001; Wang, 2006). In general, data mining is a data analytical technique that assists businesses in learning and understanding their customers so that decisions and strategies can be implemented most accurately and effectively to maximize profitability. Data mining is not general data analysis, but a comprehensive technique that requires analytical skills, information construction, and professional knowledge. Businesses are now facing globalized competition and are being forced to deal with an enormous amount of data. The vast amounts of data and the increasing technological ability to store them also facilitated data mining. In order to gain a certain level of competitive advantage, businesses now commonly adopt a data analytical technology called data mining. Nowadays, data mining is more widely used than ever before, not only by businesses who seek profits, but also by nonprofit organizations, government agencies, private groups, and other institutions in the public sector. Organizations use data mining as a tool to forecast customer behavior, reduce fraud and waste, and assist in medical research.
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