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.

Properties of Data Mining:

  • Discovering different patterns.
  • Prediction of Outcomes.
  • Creating actionable information.

Data Mining Process

Data Mining Process

Business Understanding

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 first step is to understand the business and the client’s needs. It is very important to understand these things for best results.
  • The next step is to clarify the current data mining scenario with the help of considering factors like resources, assumptions, constraints, and other important and relevant factors.
  • After that, there is a need to create data mining goals in order to achieve the desired business results.
  • Finally, the last step is to plan. The data mining plan should be very elaborated, and it should also fulfill the data mining goals.

Data Understanding

The data understanding is basically the process of understanding and exploring data for further judgments.

  • The first step involved in this data understanding stage is to collect data. This data needs to be collected from various available sources of data. Then some important activities, like data integration and data loading, are needed to be performed. After all, this is completed, then this data collection is successfully finished.
  • In the next step now, the property “gross” or “surface” of a data is deeply looked into, and it is very deeply explored, and finally, it is reported.
  • After the successful completion of the first two steps, then the data is needed to be explored. This is done by taking data mining questions with the help of using query, report, and visual.
  • The last step is to ensure the data quality by answering some important questions such as “Is there any missing values in the data?” and so on.

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.

  • The first and foremost step involved in this stage is choosing the modeling techniques that need to be used to prepare sets of data.
  • In the next step, the test scenario is generated so as to check the quality and validity of the model.
  • After this, run the model on the prepared set of data.
  • And the last step involved in this stage is to assess the models carefully in a way that the model created meets with every goal that the client and the business objective has asked for.

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:

  • The information that was uncovered during the process of data mining has to be mannered. So that the people who are non-technical stakeholders understand every small detail easily.
  • An elaborated and well thought deployment plan needs to be created.
  • A report has to be created, which contains all the important information during the project. The report should have a list of lessons learned and the key experiences involved during the process of data mining.

Challenges faced in Data Mining

Listed below are the various challenges that are generally faced in the process of Data Mining.

  • Data Mining usually operates on huge databases, and the data collection is also very difficult to supervise.
  • The process of data mining process require domain experts. These experts are very difficult to find.
  • Integration from heterogeneous databases is very difficult to understand, and so it can be a complex process.
  • In the case of data mining, the data set needs to be diverse. If it is not diverse, then the results can be inaccurate or imprecise.

Conclusion:

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.

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