In straightforward terms,
data mining is the process of extracting hidden and potentially significant information from massive sets of statistics. By analyzing and exploring large sets of data (with the use of multiple software), meaningful patterns and rules can be discovered.
Data mining has multiple applications across numerous fields, such as database marketing, credit risk management, sentiment analysis, healthcare bioinformatics, fraud detection, training and support, qualitative data mining, and
spam filtering. With the help of data mining, businesses can learn essential details regarding their customers and in turn, develop new and effective strategies. The data mining process includes six steps. These steps are accepted within every process of implementation.
Understanding Data – The first and foremost step is to establish the goals of a business project. A successful data mining plan is not only detailed but also fulfills business goals.
Accumulating Data – This step includes collecting all available and appropriate information (using visualization tools) of the company.
Preparing Data – This preparation step takes up most of the time of the mining process (up to 90%). This step involves the organizing, formatting, and transformation of data. Competent data mining processes contain failsafe measures so that information can be stored permanently.
Data modeling – This process includes the use…