Evolution of Data Mining

Evolution of Data Mining

In computer science, data mining, also called knowledge discovery in databases, is the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines statistics and artificial intelligence tools (such as neural networks and machine learning) with database management to analyze large digital collections, called data sets. The evolution of Data mining happened to promote usage in the industry (insurance, finance, retail), science (astronomy, medicine) research, and government protection (criminal and terrorist detection).

Origins and early applications

As the capacity of computer storage increased during the 1980s, many companies started to store more transactional data. The resulting sets of information also referred to as data warehouses, were too vast for conventional statistical methods to be analyzed. Several computer science conferences and workshops were held to examine how recent evolutions in the field of artificial intelligence(AI) —such as discoveries from expert systems, genetic algorithms, machine learning, data mining, and neural networks — can be applied to the exploration of knowledge (the preferred word in the computing community). The cycle contributed to the First International Conference on Information Discovery and Data Mining, held in Montreal in 1995, and the publication of the Evolution of Data Mining and Knowledge Discovery journal in 1997. This was also the time of the development of several early data mining companies and the launch of goods.

Credit-card-fraud detection was one of the first popular applications of data mining evolution, perhaps second only to marketing research. Typically a traditional trend becomes evident when observing a consumer’s purchasing behavior; transactions made outside of this trend may then be flagged for later review or to refuse a transaction.

However, this is made difficult by the wide range of common behaviors; no particular distinction between legitimate and deceptive behaviors works for everyone or all the time. Each individual is likely to make some purchases that differ from the types he ‘s made before, so relying on what’s normal for a single person is likely to give too many false alarms. One solution to reliability enhancement is first to group individuals with common purchasing habits because group models are less prone to minor anomalies. For example, a group of “frequent business travelers” may have a trend that involves unusual purchases at different locations, but members of this group may be flagged for other transactions, such as catalog purchases, that do not match the profile of that category.

Evolution of Data mining, with one engine competing over problem issues. For example, Netflix, an American company that provides movie rentals distributed by mail or streaming over the Internet, began the contest in 2006 to see whether anyone could develop their recommendation system by 10 percent, an algorithm for predicting the film tastes of a person based on preceding rental data.

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