Introduction to Decision Trees - Data Mining with Decision Trees: Theory and Applications - page 5

Fig. 1.2 The process of KDD.
process has many “creative” aspects in the sense that one cannot present
one formula or make a complete taxonomy for the right choices for each
step and application type. Thus, it is necessary to properly understand the
process and the different needs and possibilities in each step.
The process starts with determining the goals and “ends” with the
implementation of the discovered knowledge. As a result, changes would
have to be made in the application domain (such as offering different
features to mobile phone users in order to reduce churning). This closes the
loop and the effects are then measured on the new data repositories, after
which the process is launched again. In what follows is a brief description
of the nine-step process, starting with a managerial step:
1. Developing an understanding of the application domain. This
is the initial preparatory step that aims to understand what should
be done with the many decisions (about transformation, algorithms,
representation, etc.). The people who are in charge of a data mining
project need to understand and define the goals of the end-user and the
environment in which the knowledge discovery process will take place
(including relevant prior knowledge). As the process proceeds, there
may be even revisions and tuning of this step. Having understood the
goals, the preprocessing of the data starts as defined in the next three