Data mining can be defined as the process harvesting and discovering useful and valuable information through the analysis of enormous amounts of data found in databases, websites or data warehouses through the use of a number of techniques such as artificial intelligence, statistical and machine learning. It is a relatively a new and promising technology.
Many organizations have employed data mining, such types organizations include manufacturing, chemical, aerospace, marketing and many others. The needs of data mining processes have increased greatly. It is important to note that the data mining process ought to be repeatable and reliable by the business experts who may have little knowledge or no data mining background at all.
In the year 1990, there was a cross-industry standard process for data mining is known as (CRISP-DM) which was published after many contributions and workshops for over 300 organizations. In the next paragraphs, we examine the process for data mining in finer details. The process is made up of six phases which are cyclical.
It is important to note that understanding of the business goals clearly and finding out what your client needs to achieve are necessary steps in the data mining. After understanding the goals there is a need to assess the situation your business is currently in. After this, it is possible to analyze and create data mining goals so as to achieve the business goals within the situation a business it is currently in. therefore a good data mining plan ought to achieve both business and data mining goals. This plan needs to be detailed as much as possible and has a clear procedure of performing the project including the initial selection of the data mining tools and techniques.