DATA MINING 101
What is data mining and why is it important?
Data mining is a set of processes to make sense of complex and large amounts of information and to reach the basis of this information . The information that needs to be understood is increasing day by day. 90% of the data in the world were produced after 2014, and this size increases twice every 1.2 years. Therefore, data mining is an indispensable field for almost all companies and is strategically important. By better understanding the data, these companies can characterize their customers. Also, by better understanding the links between data, it can change sales strategies in this context . Data mining includes data diversity, the challenges associated with this diversity, and solutions to problems that arise while developing data mining applications. Overcoming these problems is very important for these applications to work effectively and efficiently . In data mining, which is a multidisciplinary field, the process of interpretation of information is done using machine learning and statistical techniques.
The process in data mining works as follows.
- Data acquisition
- Data cleaning
- Data reduction
- Data conversion
- Training and testing phase
- Evaluation of the results
Figure 1: Data mining 
What are data mining applications?
Marketing: Applications created based on data mining using marketing-related data can be used for customer segmentation. These applications are based on parameters such as the age, gender and the products they like. Advertising strategies can be developed according to the results obtained, and products can be produced according to customer density.
Banking: Banks use data mining to better understand the risks in the markets and determine their positions accordingly.
Television and Radio: Television and radio use real-time data mining to measure their audience. Broadcast and advertising policies are established with their viewing preferences and their relevance to ad types.
Medicine: Data mining is used in medicine to provide a more accurate diagnosis. Medical records, exams, and having patient medical information can be used to create an application. With this application, the best type of treatment and duration of treatment can be determined with high precision. In addition, diseases can be predicted regionally by using disease distributions and people’s information in certain regions. In this direction, by applying environmental and medical approaches, maximum efficiency can be obtained in terms of cost and health.
What are data mining applications in medicine?
With the developing technology, health data can now be stored electronically. The resulting data sets can be used in data mining applications. Even though data can be easily stored in computers, the large amounts generated require proper filtering and interpretation. These needs can be met with data mining applications.
Concentrated on issues related to the evaluation of treatment effectiveness of data mining applications in medicine and health management .
Evaluation of treatment efficacy: Data mining models created with the results obtained when stored patient information and the treatments applied to these patients are evaluated together can provide the evaluation of treatment efficacy based on patient characteristics.
Health Management: For example, by evaluating the data collected in a hospital, the duration of hospital stay can be determined according to the diseases. The processes of intensive care patients can be monitored, and the data obtained with the results can be modeled according to the characteristics of the intensive care processes of the patients.