Data Mining Process
Data mining is a process that can be defined as a process of extracting or collecting the data that is usable from a large set of data.
Data Mining has many other names, such as KDD (Knowledge Discovery in Databases), Knowledge Extraction, Data/Pattern Analysis, Data Archeology, Data Dredging, Information Harvesting and Business Intelligence.
As the name suggests, the data expert needs to understand the basic idea of the business and the client’s needs in this stage.
The data understanding is basically the process of understanding and exploring data for further judgments.
Data Preparation:
This stage is mainly for making production-ready.
The first step is to select, clean, transform, format, anonymize, and construct the data from different sources and mold it into the desired form. This stage is very crucial as it uses approximately 90% of the time of the project.
Modeling:
This stage is basically responsible and used for determining the data patterns or trends.
Evaluation:
The evaluation stage is also very important in this process. In the evaluation stage, the result or the model is evaluated against the need of the client. In this stage, new requirements for the business are raised. This may be because of the fact that new patterns are discovered due to some factors or any such other reason.
Gaining business understanding is an interactive process in data mining. Also, in this stage, the go- or no-go decisions are made. This is an important part of the deployment stage.
Deployment:
This stage is the last stage involved in the process of data mining. In this stage, data mining discoveries are pushed to the business stage. However, in this stage, the user needs to take care of some details, which are listed below:
Listed below are the various challenges that are generally faced in the process of Data Mining.
As it is already evident from the above section that Data Mining is an iterative process. In this, the mining process can be refined, and new data can be integrated to get more efficient results. Data Mining meets the requirement of effective, scalable and flexible data analysis.
In the next section of this article, it was told that the Data mining process consists of business understanding, Data Understanding, Data Preparation, Modelling, Evolution, Deployment. Data mining processes are usually performed on any data, such as database data and advanced databases. And finally, many challenges are there in the data mining process, which were listed in the last section of this article.