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Informetrics address the issue
of applying metric or quantitative information analysis methods
(i.e. statistics, probabilities and multivariate data analysis)
to produce useful information. The aim of our activity is performing
the analysis of information by computer using descriptive statistics,
cluster analysis and cartography (or mapping) algorithms which
represent the generated clusters in the form of maps. Index terms
or keywords, clusters, and maps play an analytical role in the
information analysis processing. The index terms signifying the
content knowledge in data, the clusters showing the topics or
themes and interest centers around which data can be aggregated,
and maps providing the visualization of the relative positions
of clusters in the analyzed knowledge space. Accordingly we call
them " knowledge indicators. " We have applied this approach to
the domain of scientific and technical information, i.e. stored
publications and patents in databases (for details, see Polanco
et al, 1995; 1998a; 1998b in References).
We are concerned with cartography algorithms,
which represent the clusters in the form of maps. Clustering,
cartography, and hypertext generation are the three components
of our approach (see Grivel et al, 1997 in References). Informetric
analysis of the information is divided into two phases. The first
involves the cluster generation using clustering procedures, in
which learning is unsupervised (the user does not define classes),
while the second consists of positioning the clusters on a global
map in order to display the topical organization of knowledge.
These two phases are data driven. A hypertext interface generator
provides the user with a user-friendly interface displaying the
global map, the topics or clusters and the documents set and then
it gives access to useful information organized by topics (clusters).
The maps are "visualization-based analysis
tools". In the context of data mining and knowledge discovery
in databases, Brachman and Anand (1996) have noted that " The
visualization produced is by itself a model, and the user can
examine the visualization to determine its explanatory power (...)
Appropriate display of data points and their relationships can
give the analyst insight that is virtually impossible to get from
looking at tables of output or simple summary statistics. In fact,
for some tasks, appropriate visualization is the only thing needed
to solve a problem or confirm a hypothesis, even though we do
not usually think of picture-drawing as a kind of analysis."
An optimal way of measuring by yourself the
potentiality of the visualization tools that have been applied
in EICSTES is to see them functioning in:
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