This technique is often referred to as Market Basket Analysis. placing the high-profit diapers next to the high-profit beers). Though explaining this interrelation might be difficult, taking advantage of it, on the other hand, should not be hard (e.g. Through intensive analysis of the transactions and the goods bought over a period of time, analysts found that beer and diapers were often bought together. Another widely used (though hypothetical) example is that of a very large North American chain of supermarkets. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company. The sales department will look at that information and may begin direct mail marketing of silk shirts to that customer, or it may alternatively attempt to get the customer to buy a wider range of products. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make a correlation between that customer and silk shirts. It is usually associated with a business or other organization's need to identify trends.ĭata mining involves the process of analysing data to show patterns or relationships sorting through large amounts of data and picking out pieces of relative information or patterns that occur e.g., picking out statistical information from some data.Ī simple example of data mining is its use in a retail sales department. Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts. Data mining can be defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases".
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