Data mining can be described as the process of turning raw data into useful information by examining the relationship between key data sets.
One of the main reasons why data mining has drawn a considerable degree of attention in recent times is due to the fact that there are vast amounts of data and an increasing necessity to turn that data into knowledge that is useful.
Data mining involves the extraction of knowledge through the examination of huge amounts of data/information using refined modelling systems. The information collected may be used in various applications stretching from market study, business administration, fraud detection and science exploration, to production control and engineering design. Data mining is not without its setbacks, as it can be complicated by issues such as unavailability of data, performance hiccups and mining methodology, to mention a few.
The following are essential data mining tips that should be considered for effective data mining:
Consider background knowledge
Background knowledge is essential to the discovery process and defining/understanding the discovered patterns.
Build in interactivity
The processes involved in data mining need to be interactive to enable stakeholders concentrate primarily on the examination of patterns that will yield the desired results.
Include various types of knowledge from databases
It is essential for the user to cover an extensive range of knowledge/data sets during the data mining process.
Arrange and visualise the results obtained from data mining
The arrangement and visual presentation of data mining results should be articulated in visual languages/pictures that are easily understandable.
Manage incomplete data
Data mining requires data cleaning methods to manage incomplete data and to enhance the accuracy of discovered patterns. A lack of data cleaning in the mining process diminishes the accuracy of discovered patterns.
Evaluate the patterns
Discovered patterns presented after the data mining process should be unique. If not, it may indicate a lack of originality.
Ensure the data mining process is scaleable and effective
There is need for the data mining process to be scaleable and effective in order to efficiently extract the information needed from the vast amount of data available.
Picture Attribution: “Economical Stock Market Graph” by hywards/Freedigitalphotos.net