Before tackling a data mining problem, some considerations must be take into account in order to get good interpretations of the results.
Strong correlations of data do not necessarily prove a cause-and-effect link.
It is important to remember that the predictive relationships discovered through data mining are not necessarily causes of an action or behavior.
For example;
Business user may already be aware of important patterns as a result of working with your data over time.
Data mining can confirm or qualify such empirical observations in addition to finding new patterns that may not be immediately discernible through simple observation.
Data mining does not automatically discover solutions without guidance. The patterns you find through data mining will be very different depending on how you formulate the problem.
To obtain meaningful results, you must learn how to ask the right questions. For example, rather than trying to learn how to “improve the response to a direct mail solicitation,” you might try to find the characteristics of people who have responded to your solicitations in the past.
Data mining algorithms are often sensitive to specific characteristics of the data:
To ensure meaningful data mining results, you must understand your data.
Some data preparation can automatically be performed when required by the algorithm. But some of the data preparation is typically specific to the domain of the data mining problem.
You need to understand the data that was used to build the model in order to properly interpret the results when the model is applied.