What are the stages of KDD in data mining?

Hello Friends, In this blog post(What are the stages of KDD in data mining) we are going to discuss the KDD stages in data mining. Here KDD stands for knowledge discovery in database.

The stages of KDD, which begin with the raw data and finish with the extracted knowledge, are given below.|What are the stages of KDD in data mining|

What are the stages of KDD in data mining:


This stage is related to selecting or differentiating data that are relevant to some criteria. For instance, for credit card customer profiling,…

… we extract the type of transactions for each type of customer and we might not be interested in details of the shop where the transaction took place.


Preprocessing is the data cleaning stage where unnecessary information is removed. For instance, it is unnecessary to note the sex of a patient when studying pregnancy.

When the data is drawn from several sources, it is possible this the same information is represented in different sources in different formats. This stage reconfigures the data to ensure a consistent format since there is a possibility of inconsistent forms.


The data is not only transferred across but transformed in order to be suitable for the task of data mining. In this stage, the data is made usable and navigable.

Data Mining:

The stage is related to the extraction of patterns from the data.

Interpretation and evaluation:

The manners acquired in the data mining stage are changed into knowledge, which in turn, is used to help decision-making.

Data Visualization:

Data visualization makes it possible to the observer to gain a deeper, more intuitive knowledge of the data, and as much may work well alongside data mining.

Data mining permits the observer to concentrate on certain patterns and trends and explore them in-depth using visualization.

On its own, data visualization may be overwhelmed by the volume of data in a database but in coordination with data mining, may help with exploration.

Data visualization supports users to examine large volumes of data and find out the patterns visually. Visual display of data like maps, charts, and other graphical representation permits data to be presented compactly to the users.

A single graphical screen can encode as much information as a far larger number of text screens.

As an example, when a user wants to find out whether the production problems at a plant are related to the location of the plant, the problem location can be encoded in a special color, say red, on a map.

The user may then discover locations in which the problems have occurred. Users may then form a hypothesis about why problems have occurred in those locations and may check the hypothesis against the database.

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