Atelier Visualisation et extraction de connaissances - Irisa

CA has successfully been used too for content based image analysis (Pham et al, 2009a)
or image retrieval (Pham et al, 2009b).
3 Interactive exploration of telecommunication data
3.1 Preprocessing
Due to the nature of telecommunication data (some customers never make a call, other
use a single service) we have proposed some criteria for constructing the contingency table.
- Data are collected every three months. When customers will need to use the service,
it needs to be repeated in months to form the habit of using the service and also reflects
the customer relation. A period of 3 months is sufficient to form the habit of
using customer service.
- Customers who use less than 3 services will be ignored. It is easy to analyze these
customers by focusing on the services used.
- Only services which are used at least 3 times are selected.
We have, after this step, a contingency table crossing customers and services. The element
fij of this table represent how often the customer i uses the service j.
3.2 Projection on factorial plan
In CA, customers and services are displayed on the same plane. Here, a dot “customer” is
displayed as a red circle; and a dot “service” as a blue circle. User can select one or a group
of customers (or services) by pointing on it.
To help the user for interpretation, the label of the service is displayed besides the corresponding
dot “service”. This gives us immediately a general summary of the group of services,
e.g. international call.
For focusing on the interesting customers and/or services, we display only the customers
and/or services whose contributions to inertia are high, usually 2 or 3 times the average contribution.
Other customers/services will not be displayed. The total inertia on one axis is
equal to the eigenvalue associated to this axis. The threshold is easy to determine. User can
also change displayed axes (hence plan) in order to discover more group of customers/services.
The choice of displayed axes could be done using the quality of representation
of customer/service variables. The quality of representation of a projected point i (service i)
on the axis j is the square cosine of the angle between the original point i and its projection
on axis j. If the square cosine is close to 1, this means that the position of the projected point
is close to the original position.
3.3 Hierarchical CA by visualization
We introduce here a method which allows us to discover hierarchical groups of services.
The method is based on an interesting property of CA: the simultaneous representation of
lines (customers) and columns (services). When CA projects customers and services on the
same plan, their association is interpreted by the distance (in the plan) among them. The
points “services” around a point or a group of point “customers” indicate that there is a
strong relation between these customers and services. We can apply another CA on these
F.Poulet, B.Le Grand : 9e Atelier Visualisation et Extraction de Connaissances 15