Knowledge Discovery in Databases (KDD) in Data Mining

The term, KDD in data mining, was mentioned the first time by Piatetsky-Shapiro in 1989 to describe the process through which it extracts useful information from the database that has been unknown until now.

What is KDD in Data Mining Based On?

KDD in data mining is an iterative process that analyzes patterns based on three factors –

• Importance
• Usability
• Understandability

KDD involves a set of defined stages for the treatment of the data before applying the different data mining techniques in the search for hidden patterns in them to finally make the analysis of the patterns found and finally give a useful output.

The purpose of KDD is the interpretation of patterns, models, and a deep analysis of the information that an organization has gathered to make better decisions. While data mining by itself does not need extensive research in the area in which it is managed, this technique requires careful evaluation of observable data. This includes behavior, needs, customs, user query, searches by users, etc.

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Steps Involved in KDD

The KDD involves 9 steps and their sequence is important for obtaining the expected results. In some cases, it may be necessary to return after the identification of an opportunity for improvement in the processing of the data.

The essential steps of KDD (Knowledge Discovery in Databases) are:

1 – Understanding the Data Set

Not everything is mathematics and statistics, but understanding the problems we are going to face and having context to propose viable and real solutions is. It is important to know the properties, limitations, and rules of the data or information understudy, and define the goals to be achieved.

2 – Data Selection

From the set of data collected and the objectives to be achieved already defined, available data must be chosen to carry out the study and integrate them into a single one that can help to reach the objectives of the analysis. Many times this information can be found in the same source or can also be distributed.

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3 – Cleaning and Pre-processing

At this stage, the reliability of the information is determined, that is, carrying out tasks that guarantee the usefulness of the data. For this, the data cleaning is done (treatment of lost data or removing outliers). This implies eliminating variables or attributes with missing data or eliminating information not useful for this type of task such as text, images, and others.

4 – Data Transformation

At this stage, the quality of the data is improved with transformations that involve either dimensionality reduction (reducing the number of variables in the data set) or transformations such as converting the values ​​that are numbers to categorical (discretization).

5 – Select the Appropriate Data Mining Task

In this phase, the right data mining process can be chosen – be it classification, regression, or grouping, according to the objectives that have been set for the process.

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6 – Choice of Data Mining Algorithms

Subsequently, we proceed to select the technique or algorithm or both, to search for the pattern and obtain knowledge. The meta-learning focuses on explaining the reason why an algorithm works better for certain problems, and for each technique, there are different possibilities of how to select them. Each algorithm has its own essence, its own way of working and obtaining the results, so it is advisable to know the properties of those candidates to use and see which one best fits the data.

7 – Application of Data Mining Algorithms

Finally, once the techniques have been selected, the next step is to apply them to the data already selected, cleaned, and processed. It is possible that the execution of the algorithms in several trying to adjust the parameters that optimize the results. These parameters vary according to the selected method.

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8 – Evaluation

Once the algorithms have been applied to the data set, we proceed to evaluate the patterns that were generated and the performance that was obtained to verify that it meets the goals set in the first phases. To carry out this evaluation there is a technique called Cross-Validation, which performs data partition, dividing it into training (which will be used to create the model) and test (which will be used to see that the algorithm really works and does its job well).

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9 – Interpretation

If all the steps are followed correctly and the results of the evaluation are satisfied, the last stage is simply to apply the knowledge found to the context and begin to solve its problems. If otherwise, the results are not satisfactory then it is necessary to return to the previous stages to make some adjustments, analyzing from the selection of the data to the evaluation stage.

Results must be presented in an understandable format. For this reason, data visualization techniques are important for the results to be useful since mathematical models or descriptions in text format can be difficult for end-users to interpret.

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Conclusion

As we mentioned before, the storage of data in the different information systems has been increasing dramatically, moving away from human possibilities to extract useful information efficiently. For this reason, it is necessary to use a method that helps people interpret the information stored in these huge databases and be able to extract new knowledge. KDD is one such methodology that helps in accurate information extraction and is being widely adopted by data-dependent companies globally.

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