![]() The prominent data mining examples include Delta, a major US airline, which uses data mining to analyze all reviews and comments from their customers on Twitter, sending negative feedback straight to the support. It is definitely an investment, but the fruits it bears are worth it. These invaluable data findings allow progressive brands to build better ads and user interfaces, therefore staying ahead of their competitors and earning drastically more money.ĭata mining is no longer just a good idea to consider, companies are adapting it, and adapting it fast. Information subtracted as a result of a data mining process allows ecommerce companies to find correlations between their customers’ profiles, behaviour, items they’ve selected, elements that triggered purchases and so on. Here’s what is data mining, also known as knowledge discovery, today:ĭata mining is the process of analyzing large data sets in order to find patterns, performed by machine learning, statistics, and database systems techniques. ![]() The times have drastically changed, as now data mining does no longer resemble a pursuit of a fleeing prey, but a thorough assessment of data by an all-seeing algorithm. It reminded them of angling for a haul without having any prior hypothesis established. The earliest documented data mining techniques occurred in the early 1700s with Bayes’ theorem (a formula to calculate events probability) and later in the 1800s with regression analysis (a process of dependent and independent variables relationships estimation).īefore calling it data mining, data scientists used to call it data “fishing” or “dredging”, and unfavoured it as a bad practice. People have played around with numbers in order to find correlations and patterns for hundreds of years. Before jumping ahead to the modern data mining definition, let’s look into how it all began.
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