Introduction to Decision Trees - Data Mining with Decision Trees: Theory and Applications - page 3

to be collected. The number of selected variables was usually limited and the
collection of their values could be done manually (e.g. utilizing hand-written
records or oral interviews). In the case of computer-aided analysis, the
analyst had to enter the collected data into a statistical computer package
or an electronic spreadsheet. Due to the high cost of data collection, people
learned to make decisions based on limited information.
Since the dawn of the Information Age, accumulating and storing data
has become easier and inexpensive. It has been estimated that the amount
of stored information doubles every 20 months [ Frawley et al . (1991) ] .
Unfortunately, as the amount of machine-readable information increases,
the ability to understand and make use of it does not keep pace with its
growth.
1.3 The Four-Layer Model
It is useful to arrange the data mining domain into four layers. Figure 1.1
presents this model. The first layer represents the target application. Data
mining can benefit many applications, such as:
(1) Credit Scoring — The aim of this application is to evaluate the credit
worthiness of a potential consumer. Banks and other companies use
credit scores to estimate the risk posed by doing a business transaction
(such as lending money) with this consumer.
Fig. 1.1 The four layers of data mining.