| || || IDAMS available in French and Spanish|
Now the IDAMS software "speaks" also French and Spanish!!! Both the
User Interface, including Graphid, and printed results of program execution
can appear in English, French or Spanish. The User Manual is also available
in these three languages. French and/or Spanish versions can be added when
the English one is already installed.
| || || UNESCO International Training Seminar in 1998|
As every year, CII/INF is organizing International Training Seminar: Introduction
to IDAMS. The basic part of this Seminar will take place from
Monday 23 through Wednesday 25 November 1998 at UNESCO Headquarters,
Paris. Its purpose is to introduce the latest version of the IDAMS software
package. Participants willing to extend their knowledge and/or perform more
exercises could continue individual work on 26 and 27 November. The
IDAMS development staff and computer rooms will remain at their disposal
during this period.
English will be the main language of this Seminar although explanations in
French can also be provided. Training material will be available in both
English and French.
There is no registration fee, but all other costs (travel, board, lodging)
are at the charge of participants.
| || || ICSOPRU Data Base available on diskettes|
ICSOPRU is the acronym of an UNESCO international research project on the
management, effectiveness and productivity of research teams and institutions
to which they belong, conducted by the Organization between 1971 and 1989.
Seventeen countries took part in this project, namely:
in Africa: Ghana, Nigeria
in the Arab States: Egypt
in Asia: China, India, Republic of Korea
in Europe: Austria, Belgium, Finland, Hungary, Poland, Spain,
in Latin America: Argentina, Brazil, Mexico.
An approach from multiple perspective was adopted as theoretical foundation
of the project, taking advantage of recent developments in a series of domains
such as system analysis, organizational psychology, sociology of sciences,
managerial sciences. From the methodological point of view, it was decided
to collect - through direct interviews with research teams' heads and members,
and with institutions' heads opinions and facts on a number of factors
supposed to govern their scientific productivity and to influence the impact
of their work. The same questionnaires, carefully translated into national
languages, were administered in all countries participating in the same "round"
of the study.
The common methodology (internationally developed for the ICSOPRU) was based
on standard procedures: hypothesis formulation, construction of measuring
instruments (i.e. questionnaires), sampling design, collection of data in
standardized ways from large and heterogeneous population of R&D units,
data verification and correction, construction of standard data files, etc.
These ICSOPRU procedures constituted a technical Guidebook, constantly kept
The international comparability of the study results mainly from the respect
on the part of each country of these procedures especially as regards:
the use of a set of questionnaires internationally developed for the study
the administration of the questionnaires using similar interview techniques
the verification and correction of the data, and the construction of computer
files using the same techniques and programs.
Taking into account the scientific interest that the ICSOPRU data may present
for other decades, an effort was made to deposit international ICSOPRU data
and relevant documentation at UNESCO Archives, and in a number of external
institutions. The complete set of ICSOPRU data prepared for archiving in
1990 consisted of four tapes containing international data files in IDAMS
and SPSS formats. Because of continuous development of microcomputers, the
international ICSOPRU data files in IDAMS format were transferred
to more popular media and are now available on diskettes.
ICSOPRU data and corresponding documentation can be obtained form UNESCO
Archives, upon request addressed to:
Mr J. Boel
UNESCO, Archives Records Management & Micro Division
7, Place de Fontenoy
F 75352 PARIS 07 SP, France
Fax: (33-1) 220.127.116.11
Internet e-mail: firstname.lastname@example.org
| || || Description of IDAMS programs and facilities|
CLUSFIND (finding groups in data)
This program was initially developed by L. Kaufman and P.J. Rousseeuw at
Center for Statistics, Vrije Universiteit Brussel, Belgium, and adapted to
IDAMS using the source from the MICROSIRIS software.
CLUSFIND performs cluster analysis of objects (cases in an IDAMS dataset
or row/column elements in an IDAMS square matrix) using one of six algorithms.
Four of these algorithms perform their actual analysis on a dissimilarity
matrix. Such a matrix can be input directly, or is calculated by the program
if a dataset, a similarity or a correlation matrix is input. With a dataset
as input, Euclidean or city block distance can be used for computing
PAM (Partitioning Around Medoids) searches for k representative objects
(medoids) which are centrally located in their clusters. The medoid is the
object for which the average dissimilarity to all the objects in the cluster
is minimal. Actually, the algorithm minimizes the sum of dissimilarities.
The selection of k medoids is performed in two phases. In the first phase,
an initial clustering is obtained by the successive selection of medoids
until k objects have been found. In the second phase, an attempt is made
to improve the set of medoids.
CLARA (Clustering LARge Applications) is also based on the search
for k medoids, but it is designed for analyzing large data sets. Thus, the
input to CLARA has to be a dataset. Internally, CLARA carries out two steps.
First, a sample is drawn from the set of objects, and divided into k clusters
using the same algorithm as in PAM. Then, each object not belonging to the
sample is assigned to the nearest among the k medoids. The quality of this
clustering is defined as the average distance between each object and its
representative object. Five samples are drawn and clustered in turn, and
the one is selected for which the lowest average distance was obtained. The
retained clustering of the entire data set is then analyzed further.
FANNY (Fuzzy ANalYsis) is a generalization of partitioning, but the
algorithm, instead of assigning an object to one particular cluster, gives
its value of membership coefficient to each cluster, and thus provides much
more detailed information on the structure of the data.
AGNES (AGglomerative NESting) constructs a tree-like hierarchy, starting
with clusters of one object each, and proceeding by successive fusions until
a single cluster is obtained with all the objects. In the first step, the
two closest objects are joined to constitute a cluster with two objects,
whereas the other clusters have only one member. In each succeeding step,
the two closest clusters are merged.
DIANA (DIvisive ANAlysis) produces similar output to AGNES but constructs
its hierarchy in the opposite direction, starting with one large cluster
containing all objects. At each step, it splits up a cluster into two smaller
ones, until all clusters contain only a single element. In the first step,
the data are split into two clusters by making use of dissimilarities. In
each subsequent step, the cluster with the largest diameter is split in the
MONA (MONothetic Analysis) is intended for data consisting exclusively
of binary (dichotomic) variables. The algorithm does not use dissimilarities
between objects but directly the values of variables. At each step, one of
the variables (x) is used to split the data by separating the objects for
which x=1 from those for which x=0. In the next step, each cluster obtained
in the previous step is split further, using values of one of the remaining
variables. For each split, the variable most strongly associated with the
other variables is chosen.
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