Data Mining is often translated as Data Mining, which is actually not quite right because the word Mining should be translated as Mining and not Mining.
In context, of course, there are significant differences between mining activities compared to excavation. Excavation is an activity carried out to move a number of materials from one place to another, as a result the amount of material moved will certainly be the same as the amount of material obtained. On the other hand, mining is an activity that is much more than just moving material. In the mining process, often a person will only get a small piece of material from the results of a large excavation, but a small piece of this material has a much higher value than the material excavated. In addition, the mining process must also be preceded by study, survey, preparation and so on.
Based on the description above, Data Mining should be translated into Data Mining and not Data Mining. In data mining activities, the "mountains" that will be mined are data that has been previously collected. The goal to be achieved from this data mining activity is to obtain a number of information or knowledge of high value and can be used for the benefit of the community and organization. In essence, the main purpose of data mining is to be able to find repetitive and valuable patterns that are often hidden in piles of data.
For example, an activity that enables a person to understand after reading the phone book that the majority of people named Andi live in South Jakarta can be categorized as a data mining process. Meanwhile, finding where Andi Suhendar lives by searching for his name in the phone book is not a data mining process but can only be categorized as an ordinary query process.
Data mining is an activity and not an algorithm or a program. In the implementation of data mining activities, various techniques or algorithms are often used from various disciplines, such as statistics, artificial intelligence or machine learning.
In general, the purpose of doing data mining can be grouped into 2, namely to be able to understand more about observed data behavior, or often referred to as descriptions, and to be able to predict conditions that will occur in the future or called predictions. With the ability to be able to recognize the existence of good patterns related to behavior, relationships, data movement, it is hoped that data mining can help humans to understand more about the observed system and then anticipate the possible movement of the system in the future.
