Introduction to Decision Trees - Data Mining with Decision Trees: Theory and Applications - page 8

9. Using the discovered knowledge. We are now ready to incorporate
the knowledge into another system for further action. The knowledge
becomes active in the sense that we can make changes to the system
and measure the effects. In fact, the success of this step determines
the effectiveness of the entire process. There are many challenges in
this step, such as losing the “laboratory conditions” under which we
have been operating. For instance, the knowledge was discovered from a
certain static snapshot (usually a sample) of the data, but now the data
becomes dynamic. Data structures may change as certain attributes
become unavailable and the data domain may be modified (e.g. an
attribute may have a value that has not been assumed before).
1.5 Taxonomy of Data Mining Methods
It is useful to distinguish between two main types of data min-
ing: verification-oriented (the system verifies the user's hypothesis) and
discovery-oriented (the system finds new rules and patterns autonomously).
Figure 1.3 illustrates this taxonomy. Each type has its own methodology.
Discovery methods, which automatically identify patterns in the data,
involve both prediction and description methods. Description methods
Fig. 1.3 Taxonomy of data mining methods.