Data Mining

 Many terms in the literature refer to the process of automatically or semi-automatic analysis of a large amount of data and transforming it into usable information. One of them is data mining [1]. Data mining is the job of accessing confidential information caught in data piles. The reason why the term mining is used here is that it is analogous to the process of extracting suitable data in very large data environments.

Today, daily and historical data are stored in data warehouses. The size of these stored data is increasing day by day. For this reason, there are difficulties in making decisions by using increasingly increasing databases. Additionally, data may come from many sources. Ultimately, it is necessary to analyze the data to support planning and other functions of the institution [2]. Data mining is the search and analysis of meaningful and useful connections and rules through computer programs that will help predict the future of large amounts of data [3].

The application area of data mining is quite wide. Among these areas, There are disciplines such as: Database Systems, Data Visualization, Artificial Neural Networks, Statistics, Artificial Learning, etc…

Data mining and disciplines

Many terms are used instead of data mining. Some point out that data mining means extracting uncertain information and some means drawing a conclusion from the collected information. It is difficult to realize and determine whether a particular technique is a data mining technique. For example, some discuss statistical analysis techniques by saying that they belong to data mining techniques, others disagree. For example, with data mining, companies selling medical goods can increase sales of their products by pressing certain scores in advertising, and a credit bureau can limit its losses by selecting candidates who are eligible and free of defects in payment. Data mining can also be used to find abnormal behavior. For example, a spy agency can use this technology to identify abnormal behavior among its employees [2].

By using data mining tools, it is possible to reveal the trends and behavior patterns required in decision support systems for businesses to make more effective decisions. Unlike the tools in which classical decision support systems were used in the past, there are many different features in data mining for much more comprehensive and automated analysis.

The most important feature that data mining offers to businesses is the determination of similar trends and behavior patterns between data groups. This function is used extensively in marketing activities especially for target markets [1].

Another feature of data mining is that previously unknown information can be revealed. Thanks to data mining, it is possible to reveal information that is in data warehouses but cannot be seen in the first place. For example, by analyzing the products it sells, a company can shape its future campaigns or discover the links between the products it sells. The aim here is to find data sets that were not noticed before.

Although many methods in the field of statistics are used in data mining to investigate the probabilities of data sets in databases, it differs from known statistical methods in making inferences based on the qualitative values of objects [1]. Considering the level reached by data dimensions, it can be said that using statistical methods is more difficult when compared to data mining algorithms.

Data mining can actually be considered as the result of the natural development process of information technologies, because, in this development process, the increased use of computer networks and the Internet accelerated the development of databases and it became difficult to reach the purposeful information level of the collected data. In today’s economic conditions and fast changing environments, the risk of making wrong decisions is very high in decisions made based on work experience and hunches. The only way to reduce risk is decision support solutions that anticipate knowledge-based management. Data mining tools are indispensable tools for building a true decision support system. At this point, it has become inevitable to benefit from information technologies.

Based on all this information, it is possible to make the following definition for data mining: Data mining is the job of accessing and using data that is meaningful, which will enable us to make predictions about the future, from databases where a large amount of information is stored.

REFERENCES

[1]: İnan, O., “Veri madenciliği”, Yüksek Lisans Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Konya, 1–50 (2003).

[2]. Thuarisingham, B.M., “Web Data Mining and Applications in Business Intelligence and Counter Terrorism”, CRC Press LLC, Boca Raton FL USA, 35 (2003).

[3] Akpınar, H., “Veri tabanlarında bilgi keĢfi ve veri madenciliği”. İ.Ü. İşletme Fakültesi Dergisi, 29(1): 1–22 (2000).