Examples of use of data mining
Business
Data mining can significantly contribute to customer relationship-based business management applications. Instead of contacting the customer indiscriminately through a call center or by sending emails, only those who are perceived to be more likely to respond positively to a given offer or promotion will be contacted.
Companies that use data mining routinely see the return on investment, but also recognize that the number of predictive models developed can grow very quickly. Instead of building models to predict which customers are likely to switch, the company could build separate models for each region or for each type of customer. You may also want to determine which customers are going to be profitable during a window of time (a fortnight, a month,...) and only send offers to people who are likely to be profitable. To maintain this number of models, it is necessary to manage the versions of each model and move to data mining that is as automated as possible.
In such a changing environment where the volumes of measurable data grow exponentially thanks to digital marketing,[7] "the waits produced by the dependency between technical departments and expert statisticians mean that in the end the results of the analyzes are useless" to those responsible for business and decision making.[8] This explains why providers of data mining tools are working on easier-to-use applications in what is known as visual data mining[9] and the demand for employment of this type of user business analyst is skyrocketing in recent years. According to Gartner, it is expected that during 2016-2017 there will only be "qualified professionals to fill a third of the positions."[10].
The classic example of the application of data mining has to do with the detection of purchasing habits in supermarkets. A widely cited study found that on Fridays there were an unusually high number of customers who purchased diapers and beer at the same time. It was detected that this was due to the fact that on that day young parents usually went to the supermarket whose perspective for the weekend consisted of staying at home taking care of their child and watching television with a beer in their hand. The supermarket was able to increase its beer sales by placing the product near diapers to encourage compulsive selling.
A more common example is the detection of leak patterns. In many industries—such as banking, telecommunications, etc.—there is an understandable interest in detecting as soon as possible those customers who may be thinking about terminating their contracts to possibly switch to a competitor. To these customers—and depending on their value—personalized offers could be made, special promotions offered, etc., with the ultimate goal of retaining them. Data mining helps determine which customers are most likely to unsubscribe by studying their behavioral patterns and comparing them with samples of customers who have, in fact, unsubscribed in the past.
An analogous case is that of the detection of money laundering or fraud transactions in the use of credit cards or mobile telephone services and even in the relationship of taxpayers with the treasury. Generally, these fraudulent or illegal operations tend to follow characteristic patterns that allow, with a certain degree of probability, to distinguish them from legitimate ones and thus develop mechanisms to take quick measures against them.
Data mining can also be useful for human resources departments in identifying the characteristics of their most successful employees. The information obtained can help in recruiting personnel, focusing on the efforts of your employees and the results obtained by them. Furthermore, the help offered by the applications for Strategic Management in a company translates into obtaining advantages at the corporate level, such as improving the profit margin or sharing objectives; and in improving operational decisions, such as development of production plans "Production (Economy)") or workforce management.
Internet Behavior
It is also a popular area to analyze the behavior of visitors—especially when they are potential customers—on an Internet website. Or the use of information – obtained through more or less legitimate means – about them to offer them advertising specifically adapted to their profile. Or, once they purchase a certain product, they immediately know what else to offer them, taking into account the historical information available about the customers who have purchased the first one.
Terrorism
Data mining has been cited as the method by which the US Army's "Able Danger" unit had identified September 11, 2001, leader Mohammed Atta and three other 9/11 hijackers as possible members of an Al Qaeda cell operating in the US more than a year before the attack. It has been suggested that both the Central Intelligence Agency and its Canadian counterpart, the US Security Intelligence Service, Canadian"), have also used this method.[11].
Games
Since the beginning of the 1960s, with the availability of oracles for certain combinational games"), also called board game endings (for example, for tic-tac-toe or chess endings "Final (chess)")) with any starting configuration, a new area in data mining has opened up, which consists of the extraction of strategies used by people for these oracles. Current approaches on pattern recognition do not seem to be able to be successfully applied to the operation of these oracles. In Instead, the production of insightful patterns is based on extensive experimentation with databases on such endgames, combined with intensive study of the endgames themselves in well-designed, technically-knowledgeable problems (previous data on endgames) and John Nunn on chess endings "Endgame". (chess)").
Video games
Over the years, technologies and advances in relation to data mining were involved in different business processes and the video game industry was not left behind in this field. The need to know its consumers and their taste is a fundamental part of surviving in an environment as competitive as this one. Different data are needed before even starting the project idea for a new video game.
Large development companies have fallen under the cover of cancellations, losses, failures and in cases even bankruptcy due to mismanagement of information.
In recent years, these video game development companies understood the great importance of the content that is handled and how the consumer sees it, which is why they focused on hiring services from companies specialized in this data mining sector to be able to present quality products that the public really like based on the analysis of the information obtained over the "videogame" years of their target audience.
Science and Engineering
In recent years, data mining has been widely used in various areas related to science and engineering. Some examples of applications in these fields are:
In the study of human genetics, the primary goal is to understand the mapping relationship between parts and individual variation in human DNA sequences and variability in disease susceptibility. In simpler terms, it is about knowing how changes in an individual's DNA sequence affect the risk of developing common diseases (such as cancer). This is very important to help improve the diagnosis, prevention and treatment of diseases. The data mining technique used to perform this task is known as "multifactor dimensionality reduction".[12].
In the field of electrical engineering, data mining techniques have been widely used to monitor the conditions of high voltage installations. The purpose of this monitoring is to obtain valuable information about the state of the equipment's insulation. To monitor vibrations or analyze load changes in transformers, certain techniques are used for data grouping (clustering), such as self-organizing maps (SOM: Self-organizing map). These maps are used to detect abnormal conditions and to estimate the nature of these anomalies.[13].
Data mining techniques have also been applied for Dissolved gas analysis (DGA) in electrical transformers. Dissolved gas analysis has long been known as the tool for diagnosing transformers. Self-organizing maps (SOM) are used to analyze data and determine trends that might be missed using classical (DGA) techniques.