An Insight into Data Mining (KDD) Processes

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Until recently, the Knowledge Discovery was so successful to fulfill the need of our modern society. But recently, due to introduction of a number of different perspectives, the processes of Knowledge Discovery in Databases (KDD) changed the ways of Knowledge Discoveries.

However, the ideal processes, involved in KDD, remains the same with slight variation due to different viewpoints.

In this article, the discussion is primarily on evolution, successive development, processes and perspectives.

Once upon a time, there were great
Mathematicians, Scientists, Philosophers, Thinkers, Reformers, Artists, Musicians, Monks, Saints, Leaders, and so on. They all were quenching the thirst of the most powerful and influential factor that might change entire humanity, affect society, bring ethics, build civilization. The Knowledge to which the eternity of human being gets fulfilled with content and satisfaction. A Mathematician works out a number of mathematical analysis, modeling of problem domain to visualize and interpret the inference. A Philosopher observes the logical insights and visualize to interpret the symmetries or contradictories happenings. An Artist lays the art to visualize the sense and conveys the knowledge to Admirers. There are so many examples. The processes of Knowledge Discoveries vary in accordance with the respective contexts, scenarios, and heavily dependent on the intentional objectives.

Understanding of KDD and KDD processes is incomplete until a detailed study on the KDD processes and in turn, subprocesses within the existing processes may be identified and explained.

The most common activities that are involved in the processes of Knowledge Discoveries in Databases are listed below.

Selection of initial data —> Preprocessing of target data —> Transformation of preprocessed data —> Data Mining of transformed data —> Visualization of interesting pattern/model —> Interpretation of interesting pattern/model —> Discovery of Knowledge

It is utmost important to bear in mind that there may be additional processes or reduction in the processes as per needs, necessities and requirements of the concerned scenarios.

Overlaping of the processes may also be observed in specific situations which are though rare but might exist in the real world scenarios.

In precise words, the processes can be summed up as follow:

1. Selection: obtain the concerned data from various sources of databases
2. Preprocessing: cleanse the choosen data
3. Transformation: convert the data to common format or specified format
4. Data Mining: apply algorithms to the data for generating interesting pattern/model
5. Visualization: imagine and think about interesting pattern/model for Knowledge
6. Interpretation: evaluate and explain findings/Knowledge

The steps involved in KDD can explain further elaborative mechanism.

The first step in the Knowledge Discovery processes is collection of data from the databases.

The second step in the processes of Knowledge Discovery is refining the data, cleaning the data to be investigated, removal of all abnormalities like errors, filling the incomplete data.

The third step in the processes of KDD is to convert the data into purposeful or functional format so that the process of data mining can be initiated.

The fourth step in the process of KDD is to mine the data to evolve a useful set of data, i.e. data of desire or needed data or more precisely, generation of interesting pattern/model.

In the fifth step, the power of imagination comes into play to visualize and think about the interesting pattern/model. Visualization step/process is relatively wide topic because of vast perspectives and influential factors.

Finally, the pattern/model is interpreted meaningfully in accordance with the visualization of data. Such a visualized data is the new findings/knowledge and such a discovery is accepted as Knowledge Discovery.


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