Data Mining Techniques for Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 71

Table 3.3 The component matrix.

Component

1 2 3 4 5

PRC SMS OUT CALLS 0.89 − 0.34 − 0.17 − 0.06 − 0.10

PRC VOICE OUT CALLS − 0.88 0.36 0.11 0.15 0.11

SMS OUT CALLS 0.86 − 0.01 − 0.16 − 0.16 − 0.09

VOICE OUT CALLS 0.20 0.88 − 0.04 − 0.28 − 0.12

VOICE OUT MINS 0.26 0.86 − 0.02 − 0.29 − 0.11

GPRS TRAFFIC 0.19 0.09 0.60 0.35 − 0.48

PRC INTERNET CALLS 0.12 0.02 0.58 0.40 − 0.51

PRC OUT CALLS ROAMING 0.14 0.18 − 0.44 0.77 0.11

OUT CALLS ROAMING 0.26 0.34 − 0.46 0.66 0.08

PRC MMS OUT CALLS 0.28 0.04 0.59 0.19 0.60

MMS OUT CALLS 0.47 0.19 0.49 0.04 0.56

component matrix, presents the linear correlations between the original fields, in
the rows, and the derived components, in the columns.
The correlations among the components and the original inputs are called
loadings; they are typically used for the interpretation and labeling of the derived
components. We will come back to loadings shortly, but for now let us examine
why the algorithm suggested a five-component solution.
The proposed solution of five components is based on the eigenvalue criterion
which is summarized in Table 3.4. This table presents the eigenvalues and
the percentage of variance/information attributable to each component. The
components are listed in the rows of the table. The highlighted first five rows
of the table correspond to the extracted components. A total of 11 components
are needed to fully account for the information of the 11 original fields. That is
why the table contains 11 rows. However, not all these components are retained.
The algorithm extracted five of them, based on the eigenvalue criterion which we
specified when we set up the model.
The eigenvalue is a measure of the variance that each component accounts
for. The eigenvalue criterion is perhaps the most widely used criterion for selecting
which components to keep. It is based on the idea that a component should
be considered insignificant if it does worse than a single field. Each single field
contains one unit of standardized variance, thus components with eigenvalues
below 1 are not extracted.
The second column of the table contains the eigenvalue of each component.
Components are extracted in descending order of importance so the first one
carries the largest part of the variance of the original fields. Extraction stops at
component 5 since component 6 has an eigenvalue below the threshold of 1.