Datamining Scholarly Journals

Data mining, also called knowledge discovery in databases, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets. Data mining is widely used in business (insurance, banking, retail), science research (astronomy, medicine), and government security (detection of criminals and terrorists). The proliferation of numerous large, and sometimes connected, government and private databases has led to regulations to ensure that individual records are accurate and secure from unauthorized viewing or tampering. Most types of data mining are targeted toward ascertaining general knowledge about a group rather than knowledge about specific individuals—a supermarket is less concerned about selling one more item to one person than about selling many items to many people—though pattern analysis also may be used to discern anomalous individual behaviour such as fraud or other criminal activity. As computer storage capacities increased during the 1980s, many companies began to store more transactional data. The resulting record collections, often called data warehouses, were too large to be analyzed with traditional statistical approaches. Several computer science conferences and workshops were held to consider how recent advances in the field of artificial intelligence (AI)—such as discoveries from expert systems, genetic algorithms, machine learning, and neural networks—could be adapted for knowledge discovery.  

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