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Data mining is the process of analyzing data from different perspectives, summarizing and sorting through large data sets into useful information. Through data mining the patterns between differently sourced data are identified and a relationship is established through data analysis to solve problems as increase revenue or cost cutting.

Apart from raw analysis, data mining involves data management aspects, data pre-processing, complexity considerations, visualizations and online updating. The benefits of data mining arrive from the ability to see beyond hidden patterns that can be used to make impactful predictions for the business future trends.

Data mining can be called as a process of drilling in through large transactional data and being able to relate the patterns in between them which can basically help in predicting the future trends using the statistics. The industries that mainly use this technology are healthcare industries: to identify best practices to improve care as well as reduce the costs, financial industries: in order to identify the frauds in terms of loans and credits, and telecom industries: to focus on customer service depending on the usage data.  Apart from this, it is mostly used in the field of science and research.

The first step in data mining is collecting all the relevant information and pushing it all into the data warehouse. Organizations providing retail services use data mining to improve customers service by collecting data like the products bought more often and the dates.

The second step is selecting a suitable algorithm in order to identify patterns in between the data and predict the future trends that enable the organizations to know their customer’s behavior in a better way and provide services to them.

The third step is collecting and extracting the key values. In order to be able to predict the future trends, a structure of the past data has to be generated. Then only the comparison can be made with the new information and can be evaluated.

The final step involves interpreting and reporting the results. In this process basically, the data is resolved to direct value comparison, or group comparison to pick out the specific elements. The data extracted in the earlier level can be combined to form the result.

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